serology <- read_csv("./01_data/raw/FFI.peru.serology.kmeans2.csv") %>%
  rename(ffi_is_code = Codigo) %>%
  select(-1)

## New names:
## Rows: 3996 Columns: 25
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (6): hf, upch_ei_fecha, District, Communities, upch_ei_sexo, Location dbl (19):
## ...1, Codigo, age, Plate, pfmsp119, pfama1, gexp18, glurpr2, rh220...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`

ffi_individual <- read_csv("./01_data/raw/individuals_result_share_072022.csv")

## Warning: One or more parsing issues, see `problems()` for details

## Rows: 4000 Columns: 147
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (31): ffi_is_code, ffi_is_community, ffi_is_district, ffi_is_health_fac...
## dbl  (99): ffi_is_sex, ffi_is_pregnancy, ffi_is_time_pregnancy, ffi_is_age, ...
## lgl   (5): ffi_is_trip_last_symp_other_othr, ffi_micro_result_specie, ffi_mi...
## date  (8): ffi_is_date, ffi_is_birth_date, ffi_is_antimal_drug_use_date, ffi...
## time  (4): ffi_is_time_bath, ffi_is_time_wakeup, ffi_is_time_sleep, ffi_is_t...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

ffi_household <- read_csv("./01_data/raw/household_gps_share_072022.csv")

## Rows: 980 Columns: 103
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (22): ffi_h_code, ffi_h_district, ffi_h_community, ffi_h_health_facilit...
## dbl  (77): ffi_h_tot_family, ffi_h_tot_rooms, ffi_h_tot_beds, ffi_h_mti, ffi...
## lgl   (2): ffi_h_material_floor_othr, ffi_h_fuel_kitchen_othr
## dttm  (1): ffi_gps_time
## date  (1): ffi_h_date
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

data_2000_2019 <- read_csv("./01_data/raw/Data_FFI_2000_2019_20212405.csv")

## Rows: 31795 Columns: 23
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr   (6): Health Facility Name, Institution Type, Province, District, Facil...
## dbl  (16): HFUID, RENAESID, Facility Network, Facility sub-network, Latitude...
## date  (1): Date by month
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

data_2020_2021 <- read_csv("./01_data/raw/Data_FFI_2020_2021_20210629.csv")

## Rows: 1751 Columns: 23
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr   (6): Health Facility Name, Institution Type, Province, District, Facil...
## dbl  (16): HFUID, RENAESID, Facility Network, Facility sub-network, Latitude...
## date  (1): Date by month
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

poblacion <- readRDS("./01_data/raw/poblacion_inei_2017.rds")

ffi_total <- ffi_individual %>%
  full_join(
    serology %>%
      mutate(
        ffi_is_code = paste0(0, ffi_is_code)
      ),
    by = "ffi_is_code"
  )

ffi_total <- ffi_total %>%
  mutate(
    age_cat = case_when(
      ffi_is_age_fixed >= 70 ~ "[70+)",
      ffi_is_age_fixed >= 60 ~ "[60-70)",
      ffi_is_age_fixed >= 50 ~ "[50-60)",
      ffi_is_age_fixed >= 40 ~ "[40-50)",
      ffi_is_age_fixed >= 30 ~ "[30-40)",
      ffi_is_age_fixed >= 20 ~ "[20-30)",
      ffi_is_age_fixed >= 10 ~ "[10-20)",
      ffi_is_age_fixed >= 0 ~ "[0-10)"
    ),
    age_cat = factor(age_cat),
    age_code = case_when(
      age_cat == "[70+)" ~ 8,
      age_cat == "[60-70)" ~ 7,
      age_cat == "[50-60)" ~ 6,
      age_cat == "[40-50)" ~ 5,
      age_cat == "[30-40)" ~ 4,
      age_cat == "[20-30)" ~ 3,
      age_cat == "[10-20)" ~ 2,
      age_cat == "[0-10)" ~ 1,
    ),
    gender = factor(
      ffi_is_sex,
      labels = c("Male", "Female")
    ),
    ffi_is_malaria = factor(
      ffi_is_malaria,
      labels = c("No", "Yes", "Don't Know / No Answer")
    ),
    across(
      c(pf_recent:pv_historic),
      ~ factor(., labels = c("Negative", "Positive"))
    ),
    across(
      c(
        ffi_is_fever_month,
        ffi_is_antimal_drug_use, 
        ffi_is_mosq_net
      ),
      ~ factor(., labels = c("No", "Yes"))
    ),
    ffi_is_trip_month = factor(
      ffi_is_trip_month,
      labels = c("No", "Yes", "Don't Know/No Answer")
    ),
    ffi_is_mal_lifetime = factor(
      ffi_is_mal_lifetime,
      labels = c(
        "1 to 3 times",
        "3 to 7 times",
        "More than 7 times"
      )
    ),
    ffi_is_place_shower = factor(
      ffi_is_place_shower,
      labels = c(
        "Bathroom inside the dwelling",
        "Bathroom outside the dwelling",
        "In the countryside/river",
        "Other"
      )
    ),
    education_level = case_when(
      ffi_is_inst_level %in% 0 ~ "No schooling", 
      ffi_is_inst_level %in% 1:2 ~ "Primary school",
      ffi_is_inst_level %in% 3:4 ~ "Secondary school",
      ffi_is_inst_level %in% 5:6 ~ "Higher education"
    ),
    education_level = fct_relevel(education_level, "No schooling",
                                  "Primary school",
                                  "Secondary school"),
    economic_activities = factor(ffi_is_main_econ_act,
                                 labels = c("Day labourer",
                                            "Wood extractor",
                                            "Fisherman",
                                            "Livestock farmer",
                                            "Farmer",
                                            "Trader",
                                            "Housewife",
                                            "Student",
                                            "Motorcycle taxi driver",
                                            "None",
                                            "Other")),
    pv_exposure = case_when(
      pv_recent == "Negative" & pv_historic == "Negative" ~ "Negative",
      is.na(pv_recent) & is.na(pv_historic) ~ NA_character_,
      TRUE ~ "Positive"
    ),
    pf_exposure = case_when(
      pf_recent == "Negative" & pf_historic == "Negative" ~ "Negative",
      is.na(pf_recent) & is.na(pf_historic) ~ NA_character_,
      TRUE ~ "Positive"
    ),
    recent_exposure = case_when(
      pv_recent == "Negative" & pf_recent == "Negative" ~ "Negative",
      is.na(pv_recent) & is.na(pf_recent) ~ NA_character_,
      TRUE ~ "Positive"
    ),
    historical_exposure = case_when(
      pv_historic == "Negative" & pf_historic == "Negative" ~ "Negative",
      is.na(pv_historic) & is.na(pf_historic) ~ NA_character_,
      TRUE ~ "Positive"
    ),
    only_pv_exposure = case_when(
      pv_exposure == "Positive" & pf_exposure == "Negative" ~ "Positive", 
      TRUE ~ "Negative"
    ),
    only_pf_exposure = case_when(
      pf_exposure == "Positive" & pv_exposure == "Negative" ~ "Positive", 
      TRUE ~ "Negative"
    ),
    pv_pf_exposure = case_when(
      pv_exposure == "Positive" & pf_exposure == "Positive" ~ "Positive",
      TRUE ~ "Negative"
    ),
    freedom_malaria = case_when(
      pv_exposure == "Negative" & pf_exposure == "Negative" ~ "Positive", 
      TRUE ~ "Negative"
    ),
    only_pv_recent = case_when(
      pv_recent == "Positive" & pf_recent == "Negative" ~ "Positive", 
      TRUE ~ "Negative"
    ),
    only_pf_recent = case_when(
      pf_recent == "Positive" & pv_recent == "Negative" ~ "Positive", 
      TRUE ~ "Negative"
    ),
    pv_pf_recent = case_when(
      pv_recent == "Positive" & pf_recent == "Positive" ~ "Positive",
      TRUE ~ "Negative"
    ),
    freedom_malaria_recent = case_when(
      pv_recent == "Negative" & pf_recent == "Negative" ~ "Positive", 
      TRUE ~ "Negative"
    ),
    only_pv_historic = case_when(
      pv_historic == "Positive" & pf_historic == "Negative" ~ "Positive", 
      TRUE ~ "Negative"
    ),
    only_pf_historic = case_when(
      pf_historic == "Positive" & pv_historic == "Negative" ~ "Positive", 
      TRUE ~ "Negative"
    ),
    pv_pf_historic = case_when(
      pv_historic == "Positive" & pf_historic == "Positive" ~ "Positive",
      TRUE ~ "Negative"
    ),
    freedom_malaria_historic = case_when(
      pv_historic == "Negative" & pf_historic == "Negative" ~ "Positive", 
      TRUE ~ "Negative"
    ),
    across(c(pv_exposure:freedom_malaria_historic), factor)
  )

labelled::var_label(ffi_total) <- list(
  ffi_is_district = "Districs",
  gender = "Gender",
  age_cat = "Age",
  education_level = "Education Level",
  economic_activities = "Economic Activities",
  pf_recent = "Recent P. Falciparum",
  pf_historic = "Historical P. Falciparum",
  pv_recent = "Recent P. Vivax",
  pv_historic = "Historical P. Vivax",
  pv_exposure = "P. Vivax Exposure",
  pf_exposure = "P. Falciparum Exposure"
)

saveRDS(ffi_total, 
        file = "01_data/processed/ffi_total.rds")

library(gtsummary)

fisher.test.simulate.p.values <- function(data, variable, by, ...) {
  result <- list()
  test_results <- stats::fisher.test(data[[variable]], data[[by]], simulate.p.value = TRUE)
  result$p <- test_results$p.value
  result$test <- test_results$method
  result
}

table_1 <- ffi_total %>%
  select(
    ffi_is_district,
    gender,
    age_cat,
    education_level,
    economic_activities,
    pf_recent:pv_historic
  ) %>%
  tbl_summary(
    by = "ffi_is_district",
    missing_text = "Missing"
  ) %>%
  add_n() %>%
  add_overall() %>%
  add_p(
     test = list(all_categorical() ~ "fisher.test.simulate.p.values")
  ) %>% 
  bold_p() %>%
  modify_header(label = "**Variable**") %>%
  bold_labels()
  
table1

## # A tibble: 6 × 4
##   country      year  cases population
##   <chr>       <int>  <int>      <int>
## 1 Afghanistan  1999    745   19987071
## 2 Afghanistan  2000   2666   20595360
## 3 Brazil       1999  37737  172006362
## 4 Brazil       2000  80488  174504898
## 5 China        1999 212258 1272915272
## 6 China        2000 213766 1280428583

table_1 %>%
  as_flex_table() %>%
  flextable::save_as_docx(path = "./02_output/reports/table1.docx")

 table2 <- ffi_total %>%
   select(
     gender,
     age_cat,
     education_level,
     economic_activities,
     pf_exposure,
     pv_exposure
   ) %>%
   drop_na(pf_exposure, pv_exposure) %>%
   pivot_longer(
     cols = pv_exposure:pf_exposure,
     names_to = "exposure",
     values_to = "result_exposure"
   ) %>%
   mutate(
    exposure = case_when(
      exposure == "pv_exposure" ~ "P. Vivax Exposure",
      exposure == "pf_exposure" ~ "P. Falciparum Exposure"
    )
   ) %>%
   tbl_strata(
    strata = exposure,
    .tbl_fun =
      ~ .x %>%
          tbl_summary(
            by = result_exposure,
            missing_text = "Missing"
          ) %>%
          add_p(
            test = list(all_categorical() ~ "fisher.test.simulate.p.values")
          ) %>%
          bold_p() %>%
          modify_header(label = "**Variable**") %>%
          bold_labels() 
   ) 

table2_overall <- ffi_total %>%
  select(
    gender,
    age_cat,
    education_level,
    economic_activities,
    pf_exposure,
    pv_exposure
  ) %>%
  drop_na(pf_exposure, pv_exposure) %>%
  tbl_summary(
    missing_text = "Missing"
  ) %>%
  modify_header(
    label = "**Variable**"
  ) %>%
  bold_labels() %>%
  modify_spanning_header(stat_0 ~ "**Overall**")

table2 <- tbl_merge(
  tbls = list(table2, table2_overall),
  tab_spanner = FALSE
)

table2

table2 %>%
  as_flex_table() %>%
  flextable::save_as_docx(path = "./02_output/reports/table2.docx")

 table3 <- ffi_total %>%
   select(
     gender,
     age_cat,
     education_level,
     economic_activities,
     recent_exposure,
     historical_exposure
   ) %>%
   drop_na(recent_exposure, historical_exposure) %>%
   pivot_longer(
     cols = recent_exposure:historical_exposure,
     names_to = "exposure",
     values_to = "result_exposure"
   ) %>%
   mutate(
    exposure = case_when(
      exposure == "recent_exposure" ~ "Recent Exposure",
      exposure == "historical_exposure" ~ "Historical Exposure"
    )
   ) %>%
   tbl_strata(
    strata = exposure,
    .tbl_fun =
      ~ .x %>%
          tbl_summary(
            by = result_exposure,
            missing_text = "Missing"
          ) %>%
          add_p(
            test = list(all_categorical() ~ "fisher.test.simulate.p.values")
          ) %>%
          bold_p() %>%
          modify_header(label = "**Variable**") %>%
          bold_labels() 
   ) 

table3_overall <- ffi_total %>%
  select(
    gender,
    age_cat,
    education_level,
    economic_activities,
    recent_exposure,
    historical_exposure
  ) %>%
  drop_na(recent_exposure, historical_exposure) %>%
  tbl_summary(
    missing_text = "Missing"
  ) %>%
  modify_header(
    label = "**Variable**"
  ) %>%
  bold_labels() %>%
  modify_spanning_header(stat_0 ~ "**Overall**")

table3 <- tbl_merge(
  tbls = list(table3, table3_overall),
  tab_spanner = FALSE
)

table3

table3 %>%
  as_flex_table() %>%
  flextable::save_as_docx(path = "./02_output/reports/table3.docx")

iquitos_centro <- tibble(
  "ffi_h_health_facility_name" = "Hospital Regional de Loreto",
  Longitude = -73.25385902080906,
  Latitude = -3.7264060164148716,
) %>%
  st_as_sf(coords = c("Longitude", "Latitude"), crs = 4326) %>%
  st_transform(crs = 32718)

communities_sf <- ffi_household %>%
  select(
    ffi_h_district,
    ffi_h_code_community,
    ffi_h_community,
    ffi_gps_long,
    ffi_gps_lat
  ) %>%
  st_as_sf(
    coords = c("ffi_gps_long", "ffi_gps_lat"),
    crs = 4326
  ) %>%
  group_by(ffi_h_district, ffi_h_code_community, ffi_h_community) %>%
  summarise() %>%
  st_centroid()

## `summarise()` has grouped output by 'ffi_h_district', 'ffi_h_code_community'.
## You can override using the `.groups` argument.

## Warning in st_centroid.sf(.): st_centroid assumes attributes are constant over
## geometries of x

communities_distances <- communities_sf %>%
  st_transform(crs = 32718) %>%
  st_distance(iquitos_centro)

communities_distances <- enframe(communities_distances) %>%
  mutate(
    ffi_is_cod_com = communities_sf$ffi_h_code_community,
    ffi_is_community = communities_sf$ffi_h_community,
    distance = as.numeric(value)
  ) %>%
  select(-c(name, value))

communities_distances %>%
  mutate(
    ffi_h_district = communities_sf$ffi_h_district
  ) %>%
  group_by(ffi_h_district) %>%
  slice_min(distance, n = 1) %>%
  bind_rows(
    communities_distances %>%
      mutate(
        ffi_h_district = communities_sf$ffi_h_district
      ) %>%
      group_by(ffi_h_district) %>%
      slice_max(distance, n = 1)
  ) %>%
  arrange(ffi_h_district, distance)

data_malaria <- bind_rows(
  data_2000_2019,
  data_2020_2021
) %>%
  drop_na(District)

pob_loreto <- poblacion %>%
  filter(dep == "LORETO") %>%
  select(District = distr, Total)

malaria_cases_pob <- data_malaria %>%
  rowwise() %>%
  mutate(
    cases_malaria = sum(
      c_across(c(
        `Confirmed P. Falciparum`,
        `Confirmed P. Vivax`
      )),
      na.rm = TRUE
    )
  ) %>%
  group_by(District) %>%
  summarise(cases_malaria = sum(cases_malaria))

malaria_cases_pob <- malaria_cases_pob %>%
  left_join(
    pob_loreto
  ) %>%
  mutate(
    api = cases_malaria * 1000 / Total
  )

## Joining, by = "District"

data(Peru, package = "innovar")

malaria_cases_pob_sf <- Peru %>%
  filter(dep == "LORETO") %>%
  select(District = distr, geometry) %>% 
  right_join(
    malaria_cases_pob
  )

## Joining, by = "District"

figure1 <- ggplot(malaria_cases_pob_sf) +
  geom_sf(aes(fill = api, geometry = geometry)) +
  geom_sf(data = malaria_cases_pob_sf %>% dplyr::filter(District == "BELEN"), colour = "#aa6439", fill = NA, size = 1.5, aes(fill = api, geometry = geometry)) +
  geom_sf(data = malaria_cases_pob_sf %>% dplyr::filter(District == "INDIANA"), colour = "#256e5d", fill = NA, size = 1.5, aes(fill = api, geometry = geometry)) +
  scale_fill_distiller(
    name = "API",
    na.value = "black",
    # limits = c(0, 4000),
    palette = "RdYlGn",
    direction = -1,
    breaks = scales::pretty_breaks(n = 5),
    values = c(
      0,
      0.001,
      0.002,
      0.005,
      0.008,
      0.01,
      0.02,
      0.03,
      0.05,
      0.1,
      0.6,
      0.9,
      1
    )
  ) +
  labs(
    x = NULL,
    y = NULL,
    title = NULL
  ) +
  theme_classic() +
  geom_sf_label_repel(
    data = malaria_cases_pob_sf %>% dplyr::filter(District == "BELEN"),
    aes(label = District),
    min.segment.length = 0,
    force = 100,
    nudge_x = -1,
    seed = 10,
    colour = "#aa6439"
  ) +
  geom_sf_label_repel(
    data = malaria_cases_pob_sf %>% dplyr::filter(District == "INDIANA"),
    aes(label = District),
    min.segment.length = 0,
    force = 100,
    nudge_x = 1,
    seed = 10,
    colour = "#256e5d"
  )

figure1

## Warning in st_point_on_surface.sfc(data$geometry): st_point_on_surface may not
## give correct results for longitude/latitude data

## Warning in st_point_on_surface.sfc(data$geometry): st_point_on_surface may not
## give correct results for longitude/latitude data

ggsave(
  "./02_output/plots/figure1.png",
  figure1,
  device = grDevices::png,
  dpi = 300
)

# ffi_total %>%
#   ggplot(aes(
#     x = ffi_is_community,
#     fill = pv_historic
#   )) +
#   coord_flip(ylim = c(0, 0.25)) +
#   geom_bar(position = "fill")

plot1 <- ffi_total %>%
  drop_na(pf_recent:pv_historic) %>%
  left_join(
    communities_distances
  )  %>%
  pivot_longer(
    cols = pf_recent:pv_historic,
    names_to = "malaria",
    values_to = "result_malaria"
  ) %>%
  mutate(
    malaria = case_when(
      malaria == "pf_recent" ~ "Recent P. Falciparum",
      malaria == "pf_historic" ~ "Historical P. Falciparum",
      malaria == "pv_recent" ~ "Recent P. Vivax",
      malaria == "pv_historic" ~ "Historical P. Vivax"
    ),
    ffi_is_community = str_to_title(ffi_is_community),
    ffi_is_community = paste0(
      ffi_is_community,
      " (",
      str_to_title(ffi_is_district),
      ")"
    ),
    ffi_is_community = fct_reorder(
      ffi_is_community,
      distance,
      .desc = TRUE
    )
  ) %>%
  ggplot(aes(
    x = ffi_is_community,
    fill = result_malaria
  )) +
  facet_wrap(vars(malaria)) +
  geom_bar(position = "fill", color = "black") +
  coord_flip(ylim = c(0, 0.25)) +
  scale_y_continuous(
    labels = scales::percent_format()
  ) +
  #ggsci::scale_fill_lancet() +
  innovar::scale_fill_innova("npr") +
  labs(
    x = "Communities",
    y = "Percentage"
  ) +
  guides(
    fill = guide_legend("Results")
  ) +  
  theme_minimal() +
  theme(
    strip.text = element_text(
      face = "bold",
      size = 11
    ),
    axis.title = element_text(
      face = "bold",
      size = 11
    ),
    legend.title = element_text(
      face = "bold",
      size = 11
    )
  )

## Joining, by = c("ffi_is_community", "ffi_is_cod_com")

plot1

ggsave(
  "./02_output/plots/plot1_typeofmalaria_communities.png",
  plot1,
  dpi = 300,
  bg = "white",
  width = 10,
  height = 9
)

plot1_indiana <- ffi_total %>%
  drop_na(pf_recent:pv_historic) %>%
  left_join(
    communities_distances
  )  %>%
  pivot_longer(
    cols = pf_recent:pv_historic,
    names_to = "malaria",
    values_to = "result_malaria"
  ) %>%
  mutate(
    malaria = case_when(
      malaria == "pf_recent" ~ "Recent P. Falciparum",
      malaria == "pf_historic" ~ "Historical P. Falciparum",
      malaria == "pv_recent" ~ "Recent P. Vivax",
      malaria == "pv_historic" ~ "Historical P. Vivax"
    ),
    ffi_is_community = str_to_title(ffi_is_community)
  ) %>%
  filter(ffi_is_district == "INDIANA") %>%
  mutate(
    ffi_is_community = fct_reorder(
      ffi_is_community,
      distance,
      .desc = TRUE
    )
  ) %>%
  ggplot(aes(
    x = ffi_is_community,
    fill = result_malaria
  )) +
  facet_wrap(vars(malaria)) +
  geom_bar(position = "fill", color = "black") +
  coord_flip(ylim = c(0, 0.25)) +
  scale_y_continuous(
    labels = scales::percent_format()
  ) +
  #ggsci::scale_fill_lancet() +
  innovar::scale_fill_innova("npr") +
  labs(
    title = str_wrap("Serological results by plasmodium type of malaria in the Indiana District", 100),
    x = "Communities",
    y = "Percentage"
  ) +
  guides(
    fill = guide_legend("Results")
  ) +  
  theme_minimal() +
  theme(
    strip.text = element_text(
      face = "bold",
      size = 11
    ),
    axis.title = element_text(
      face = "bold",
      size = 11
    ),
    legend.title = element_text(
      face = "bold",
      size = 11
    )
  )

## Joining, by = c("ffi_is_community", "ffi_is_cod_com")

plot1_indiana

ggsave(
  "./02_output/plots/plot1_typeofmalaria_communities_indiana.png",
  plot1_indiana,
  dpi = 300,
  bg = "white",
  width = 10,
  height = 9
)

plot1_belen <- ffi_total %>%
  drop_na(pf_recent:pv_historic) %>%
  left_join(
    communities_distances
  ) %>%
  pivot_longer(
    cols = pf_recent:pv_historic,
    names_to = "malaria",
    values_to = "result_malaria"
  ) %>%
  filter(ffi_is_district == "BELEN") %>%
  mutate(
    malaria = case_when(
      malaria == "pf_recent" ~ "Recent P. Falciparum",
      malaria == "pf_historic" ~ "Historical P. Falciparum",
      malaria == "pv_recent" ~ "Recent P. Vivax",
      malaria == "pv_historic" ~ "Historical P. Vivax"
    ),
    ffi_is_community = str_to_title(ffi_is_community),
    ffi_is_community = fct_reorder(
      ffi_is_community,
      distance,
      .desc = TRUE
    )
  ) %>%
  ggplot(aes(
    x = ffi_is_community,
    fill = result_malaria
  )) +
  facet_wrap(vars(malaria)) +
  geom_bar(position = "fill", color = "black") +
  coord_flip(ylim = c(0, 0.25)) +
  scale_y_continuous(
    labels = scales::percent_format()
  ) +
  #ggsci::scale_fill_lancet() +
  innovar::scale_fill_innova("npr") +
  labs(
    title = str_wrap("Serological results by plasmodium type of malaria in the Belen District", 100),
    x = "Communities",
    y = "Percentage"
  ) +
  guides(
    fill = guide_legend("Results")
  ) +  
  theme_minimal() +
  theme(
    strip.text = element_text(
      face = "bold",
      size = 11
    ),
    axis.title = element_text(
      face = "bold",
      size = 11
    ),
    legend.title = element_text(
      face = "bold",
      size = 11
    )
  )

## Joining, by = c("ffi_is_community", "ffi_is_cod_com")

plot1_belen

ggsave(
  "./02_output/plots/plot1_typeofmalaria_communities_belen.png",
  plot1_belen,
  dpi = 300,
  bg = "white",
  width = 10,
  height = 9
)

plot2 <- ffi_total %>%
  drop_na(pf_exposure, pv_exposure) %>%
  left_join(
    communities_distances
  ) %>%
  pivot_longer(
    cols = pv_exposure:pf_exposure,
    names_to = "exposure",
    values_to = "result_exposure"
  )  %>%
  mutate(
    exposure = case_when(
         exposure == "pv_exposure" ~ "P. Vivax Exposure",
         exposure == "pf_exposure" ~ "P. Falciparum Exposure"
    ),
    ffi_is_community = str_to_title(ffi_is_community),
    ffi_is_community = fct_reorder(
      ffi_is_community,
      distance,
      .desc = TRUE
    )
  ) %>%
  ggplot(aes(
    x = ffi_is_community,
    fill = result_exposure
  )) +
  facet_wrap(vars(exposure)) +
  geom_bar(position = "fill", color = "black") +
  coord_flip(ylim = c(0, 0.40)) +
  scale_y_continuous(
    labels = scales::percent_format()
  ) +
  # ggsci::scale_fill_lancet() +
  innovar::scale_fill_innova("npr") +
  labs(
    x = "Communities",
    y = "Percentage"
  ) +
  guides(
    fill = guide_legend("Results")
  ) +
  theme_minimal() +
  theme(
    strip.text = element_text(
      face = "bold",
      size = 11
    ),
    axis.title = element_text(
      face = "bold",
      size = 11
    ),
    legend.title = element_text(
      face = "bold",
      size = 11
    )
  )

## Joining, by = c("ffi_is_community", "ffi_is_cod_com")

plot2

ggsave(
  "./02_output/plots/plot2_typeofmalaria_communities.png",
  plot2,
  dpi = 300,
  bg = "white",
  width = 10,
  height = 9
)

plot3 <- ffi_total %>%
  drop_na(recent_exposure, historical_exposure) %>%
  left_join(
    communities_distances
  ) %>%
  pivot_longer(
    cols = recent_exposure:historical_exposure,
    names_to = "exposure",
    values_to = "result_exposure"
  ) %>%
  mutate(
    exposure = case_when(
      exposure == "recent_exposure" ~ "Recent Exposure",
      exposure == "historical_exposure" ~ "Historical Exposure"
    ),
    ffi_is_community = str_to_title(ffi_is_community),
    ffi_is_community = fct_reorder(
      ffi_is_community,
      distance,
      .desc = TRUE
    )
  ) %>%
  ggplot(aes(
    x = ffi_is_community,
    fill = result_exposure
  )) +
  facet_wrap(vars(exposure)) +
  geom_bar(position = "fill", color = "black") +
  coord_flip(ylim = c(0, 0.30)) +
  scale_y_continuous(
    labels = scales::percent_format()
  ) +
  # ggsci::scale_fill_lancet() +
  innovar::scale_fill_innova("npr") +
  labs(
    x = "Communities",
    y = "Percentage"
  ) +
  guides(
    fill = guide_legend("Results")
  ) +
  theme_minimal() +
  theme(
    strip.text = element_text(
      face = "bold",
      size = 11
    ),
    axis.title = element_text(
      face = "bold",
      size = 11
    ),
    legend.title = element_text(
      face = "bold",
      size = 11
    )
  )

## Joining, by = c("ffi_is_community", "ffi_is_cod_com")

plot3

ggsave(
  "./02_output/plots/plot3_typeofmalaria_communities.png",
  plot3,
  dpi = 300,
  bg = "white",
  width = 10,
  height = 9
)

# ffi_total %>%
#   drop_na(pf_recent:pv_historic, age_cat) %>%
#   pivot_longer(
#     cols = pf_recent:pv_historic,
#     names_to = "malaria",
#     values_to = "result_malaria"
#   ) %>%
#   mutate(
#     result_malaria = case_when(
#       result_malaria == "Positive" ~ 1,
#       TRUE ~ 0
#     ),
#     malaria = case_when(
#       malaria == "pf_recent" ~ "Recent P. Falciparum",
#       malaria == "pf_historic" ~ "Historical P. Falciparum",
#       malaria == "pv_recent" ~ "Recent P. Vivax",
#       malaria == "pv_historic" ~ "Historical P. Vivax"
#     ),
#     ffi_is_community = str_to_title(ffi_is_community)
#   ) %>%
#   group_by(ffi_is_community, age_cat, malaria) %>%
#   summarise(result_malaria = mean(result_malaria)) %>%
#   ggplot(
#     aes(
#       x = age_cat,
#       y = result_malaria,
#       color = malaria,
#       group = malaria
#     )
#   ) +
#   geom_point() +
#   geom_line() +
#   facet_wrap(vars(ffi_is_community)) +
#   theme_bw()

sero_4malaria_district <- ffi_total %>%
  drop_na(pf_recent:pv_historic, age_cat) %>%
  pivot_longer(
    cols = pf_recent:pv_historic,
    names_to = "malaria",
    values_to = "result_malaria"
  ) %>%
  mutate(
    result_malaria = case_when(
      result_malaria == "Positive" ~ 1,
      TRUE ~ 0
    ),
    malaria = case_when(
      malaria == "pf_recent" ~ "Recent P. Falciparum",
      malaria == "pf_historic" ~ "Historical P. Falciparum",
      malaria == "pv_recent" ~ "Recent P. Vivax",
      malaria == "pv_historic" ~ "Historical P. Vivax"
    ),
    across(
      c(ffi_is_community:ffi_is_health_facility_name),
      str_to_title
    )
  ) %>%
  group_by(ffi_is_district, age_cat, malaria) %>%
  summarise(result_malaria = mean(result_malaria)) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_malaria,
      color = malaria,
      group = malaria
    )
  ) +
  geom_point() +
  geom_line() +
  facet_wrap(vars(ffi_is_district)) +
  scale_y_continuous(
    labels = scales::percent_format(),
    limits = c(0, 0.40)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

## `summarise()` has grouped output by 'ffi_is_district', 'age_cat'. You can
## override using the `.groups` argument.

sero_4malaria_district

ggsave(
  "./02_output/plots/plot4_sero_4malaria_district.png",
  sero_4malaria_district,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 7.5
)

sero_4malaria_hf <- ffi_total %>%
  drop_na(pf_recent:pv_historic, age_cat) %>%
  pivot_longer(
    cols = pf_recent:pv_historic,
    names_to = "malaria",
    values_to = "result_malaria"
  ) %>%
  mutate(
    result_malaria = case_when(
      result_malaria == "Positive" ~ 1,
      TRUE ~ 0
    ),
    malaria = case_when(
      malaria == "pf_recent" ~ "Recent P. Falciparum",
      malaria == "pf_historic" ~ "Historical P. Falciparum",
      malaria == "pv_recent" ~ "Recent P. Vivax",
      malaria == "pv_historic" ~ "Historical P. Vivax"
    ),
    across(
      c(ffi_is_community:ffi_is_health_facility_name),
      str_to_title
    ),
    ffi_is_health_facility_name = paste(
      ffi_is_district,
      ffi_is_health_facility_name,
      sep = " - "
    )
  ) %>%
  group_by(ffi_is_health_facility_name, age_cat, malaria) %>%
  summarise(result_malaria = mean(result_malaria)) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_malaria,
      color = malaria,
      group = malaria
    )
  ) +
  geom_point() +
  geom_line() +
  facet_wrap(vars(ffi_is_health_facility_name)) +
  scale_y_continuous(
    labels = scales::percent_format()
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") + 
  theme_bw() +
  theme(
    axis.text.x = element_text(
      angle = 45,
      vjust = 1,
      hjust = 1
    )
  )

## `summarise()` has grouped output by 'ffi_is_health_facility_name', 'age_cat'.
## You can override using the `.groups` argument.

sero_4malaria_hf

ggsave(
  "./02_output/plots/plot5_sero_4malaria_hf.png",
  sero_4malaria_hf,
  dpi = 300,
  bg = "white",
  width = 10,
  height = 7
)

ffi_typeof_malaria <- ffi_total %>%
  drop_na(pf_exposure, pv_exposure, age_cat) %>%
  pivot_longer(
    cols = pv_exposure:pf_exposure,
    names_to = "exposure",
    values_to = "result_exposure"
  ) %>%
  mutate(
    exposure = case_when(
      exposure == "pv_exposure" ~ "P. Vivax Exposure",
      exposure == "pf_exposure" ~ "P. Falciparum Exposure"
    ),
    result_exposure = case_when(
      result_exposure == "Positive" ~ 1,
      TRUE ~ 0
    ),
    across(
      c(ffi_is_community:ffi_is_health_facility_name),
      str_to_title
    )
  )

sero_typeofmalaria_district <- ffi_typeof_malaria %>%
  group_by(ffi_is_district, age_cat, exposure) %>%
  summarise(result_exposure = mean(result_exposure)) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_exposure,
      color = exposure,
      group = exposure
    )
  ) +
  geom_point() +
  geom_line() +
  facet_wrap(vars(ffi_is_district)) +
  scale_y_continuous(
    labels = scales::percent_format(),
    limits = c(0, 0.40)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

## `summarise()` has grouped output by 'ffi_is_district', 'age_cat'. You can
## override using the `.groups` argument.

ggsave(
  "./02_output/plots/plot6_sero_typeofmalaria_district.png",
  sero_typeofmalaria_district,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 7.5
)

sero_typeofmalaria_hf <- ffi_typeof_malaria %>%
  group_by(ffi_is_health_facility_name, age_cat, exposure) %>%
  summarise(result_exposure = mean(result_exposure)) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_exposure,
      color = exposure,
      group = exposure
    )
  ) +
  geom_point() +
  geom_line() +
  facet_wrap(vars(ffi_is_health_facility_name)) +
  scale_y_continuous(
    labels = scales::percent_format()
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

## `summarise()` has grouped output by 'ffi_is_health_facility_name', 'age_cat'.
## You can override using the `.groups` argument.

sero_typeofmalaria_hf

ggsave(
  "./02_output/plots/plot7_sero_typeofmalaria_hf.png",
  sero_typeofmalaria_hf,
  dpi = 300,
  bg = "white",
  width = 13,
  height = 9
)

ffi_timeofmalaria <- ffi_total %>%
  drop_na(recent_exposure, historical_exposure, age_cat) %>%
  pivot_longer(
    cols = recent_exposure:historical_exposure,
    names_to = "exposure",
    values_to = "result_exposure"
  ) %>%
  mutate(
    exposure = case_when(
      exposure == "recent_exposure" ~ "Recent Exposure",
      exposure == "historical_exposure" ~ "Historical Exposure"
    ),
    result_exposure = case_when(
      result_exposure == "Positive" ~ 1,
      TRUE ~ 0
    ),
    across(
      c(ffi_is_community:ffi_is_health_facility_name),
      str_to_title
    )
  )

sero_timeofmalaria_district <- ffi_timeofmalaria %>%
  group_by(ffi_is_district, age_cat, exposure) %>%
  summarise(result_exposure = mean(result_exposure)) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_exposure,
      color = exposure,
      group = exposure
    )
  ) +
  geom_point() +
  geom_line() +
  facet_wrap(vars(ffi_is_district)) +
  scale_y_continuous(
    labels = scales::percent_format(),
    limits = c(0, 0.40)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

## `summarise()` has grouped output by 'ffi_is_district', 'age_cat'. You can
## override using the `.groups` argument.

ggsave(
  "./02_output/plots/plot8_sero_timeofmalaria_district.png",
  sero_timeofmalaria_district,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 7.5
)

sero_timeofmalaria_hf <- ffi_timeofmalaria %>%
  group_by(ffi_is_health_facility_name, age_cat, exposure) %>%
  summarise(result_exposure = mean(result_exposure)) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_exposure,
      color = exposure,
      group = exposure
    )
  ) +
  geom_point() +
  geom_line() +
  facet_wrap(vars(ffi_is_health_facility_name)) +
  scale_y_continuous(
    labels = scales::percent_format()
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") + 
  theme_bw()

## `summarise()` has grouped output by 'ffi_is_health_facility_name', 'age_cat'.
## You can override using the `.groups` argument.

sero_timeofmalaria_hf

ggsave(
  "./02_output/plots/plot9_sero_timeofmalaria_hf.png",
  sero_timeofmalaria_hf,
  dpi = 300,
  bg = "white",
  width = 13,
  height = 9
)

library(rlang)

## 
## Attaching package: 'rlang'

## The following objects are masked from 'package:purrr':
## 
##     %@%, as_function, flatten, flatten_chr, flatten_dbl, flatten_int,
##     flatten_lgl, flatten_raw, invoke, splice

seropositivy_summarise <- function(data, type, variable, ...) {
  if (type == "4malaria") {
    ffi_total %>%
      drop_na(pf_recent:pv_historic, {{ variable }}, ...) %>%
      pivot_longer(
        cols = pf_recent:pv_historic,
        names_to = "malaria",
        values_to = "result_malaria"
      ) %>%
      mutate(
        result_malaria = case_when(
          result_malaria == "Positive" ~ 1,
          TRUE ~ 0
        ),
        malaria = case_when(
          malaria == "pf_recent" ~ "Recent P. Falciparum",
          malaria == "pf_historic" ~ "Historical P. Falciparum",
          malaria == "pv_recent" ~ "Recent P. Vivax",
          malaria == "pv_historic" ~ "Historical P. Vivax"
        ),
        across(
          c(ffi_is_community:ffi_is_health_facility_name),
          str_to_title
        )
      ) %>%
      group_by(ffi_is_district, {{ variable }}, ..., malaria) %>%
      summarise(result_malaria = mean(result_malaria))
  } else if (type == "typeofmalaria" ) {
    ffi_total %>%
      drop_na(pf_exposure, pv_exposure, {{ variable }}, ...) %>%
      pivot_longer(
        cols = pv_exposure:pf_exposure,
        names_to = "exposure",
        values_to = "result_exposure"
      ) %>%
      mutate(
        exposure = case_when(
          exposure == "pv_exposure" ~ "P. Vivax Exposure",
          exposure == "pf_exposure" ~ "P. Falciparum Exposure"
        ),
        result_exposure = case_when(
          result_exposure == "Positive" ~ 1,
          TRUE ~ 0
        ),
        across(
          c(ffi_is_community:ffi_is_health_facility_name),
          str_to_title
        )
      ) %>%
      group_by(ffi_is_district, {{ variable }}, ..., exposure) %>%
      summarise(result_exposure = mean(result_exposure))
  } else if (type == "timeofmalaria") {
    ffi_total %>%
      drop_na(recent_exposure, historical_exposure, 
              {{ variable }}, ...) %>%
      pivot_longer(
        cols = recent_exposure:historical_exposure,
        names_to = "exposure",
        values_to = "result_exposure"
      ) %>%
      mutate(
        exposure = case_when(
          exposure == "recent_exposure" ~ "Recent Exposure",
          exposure == "historical_exposure" ~ "Historical Exposure"
        ),
        result_exposure = case_when(
          result_exposure == "Positive" ~ 1,
          TRUE ~ 0
        ),
        across(
          c(ffi_is_community:ffi_is_health_facility_name),
          str_to_title
        )
      ) %>%
      group_by(ffi_is_district, {{ variable }}, ..., exposure) %>%
      summarise(result_exposure = mean(result_exposure))      
  }  
}

sero_4malaria_district_gender <- seropositivy_summarise(
  ffi_total,
  "4malaria",
  age_cat,
  gender
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_malaria,
      color = malaria,
      group = malaria
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(gender),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    limits = c(0, 0.40)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

## `summarise()` has grouped output by 'ffi_is_district', 'age_cat', 'gender'. You
## can override using the `.groups` argument.

sero_4malaria_district_gender

ggsave(
  "./02_output/plots/plot10_sero_4malaria_district_gender.png",
  sero_4malaria_district_gender,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)

sero_typeofmalaria_district_gender <- seropositivy_summarise(
  ffi_total,
  "typeofmalaria",
  age_cat,
  gender
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_exposure,
      color = exposure,
      group = exposure
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(gender),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    limits = c(0, 0.45)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

## `summarise()` has grouped output by 'ffi_is_district', 'age_cat', 'gender'. You
## can override using the `.groups` argument.

sero_typeofmalaria_district_gender

ggsave(
  "./02_output/plots/plot11_sero_typeofmalaria_district_gender.png",
  sero_typeofmalaria_district_gender,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)

sero_timeofmalaria_district_gender <- seropositivy_summarise(
  ffi_total,
  "timeofmalaria",
  age_cat,
  gender
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_exposure,
      color = exposure,
      group = exposure
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(gender),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    limits = c(0, 0.45)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

## `summarise()` has grouped output by 'ffi_is_district', 'age_cat', 'gender'. You
## can override using the `.groups` argument.

sero_timeofmalaria_district_gender

ggsave(
  "./02_output/plots/plot12_sero_timeofmalaria_district_gender.png",
  sero_timeofmalaria_district_gender,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)

sero_4malaria_district_edulevel <- seropositivy_summarise(
  ffi_total,
  "4malaria",
  age_cat,
  education_level
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_malaria,
      color = malaria,
      group = malaria
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(education_level),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    #limits = c(0, 0.40)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

## `summarise()` has grouped output by 'ffi_is_district', 'age_cat',
## 'education_level'. You can override using the `.groups` argument.

sero_4malaria_district_edulevel

ggsave(
  "./02_output/plots/plot13_sero_4malaria_district_edulevel.png",
  sero_4malaria_district_edulevel,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)

sero_typeofmalaria_district_edulevel <- seropositivy_summarise(
  ffi_total,
  "typeofmalaria",
  age_cat,
  education_level
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_exposure,
      color = exposure,
      group = exposure
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(education_level),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    #limits = c(0, 0.45)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

## `summarise()` has grouped output by 'ffi_is_district', 'age_cat',
## 'education_level'. You can override using the `.groups` argument.

sero_typeofmalaria_district_edulevel

ggsave(
  "./02_output/plots/plot14_sero_typeofmalaria_district_edulevel.png",
  sero_typeofmalaria_district_edulevel,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)

sero_timeofmalaria_district_edulevel <- seropositivy_summarise(
  ffi_total,
  "timeofmalaria",
  age_cat,
  education_level
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_exposure,
      color = exposure,
      group = exposure
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(education_level),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    #limits = c(0, 0.45)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

## `summarise()` has grouped output by 'ffi_is_district', 'age_cat',
## 'education_level'. You can override using the `.groups` argument.

sero_timeofmalaria_district_edulevel

ggsave(
  "./02_output/plots/plot15_sero_timeofmalaria_district_edulevel.png",
  sero_timeofmalaria_district_edulevel,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)

ffi_fever_month <- ffi_total %>%
  drop_na(pf_recent:pv_historic, age_cat, ffi_is_fever_month) %>%
  pivot_longer(
    cols = pf_recent:pv_historic,
    names_to = "malaria",
    values_to = "result_malaria"
  ) %>%
  mutate(
    result_malaria = case_when(
      result_malaria == "Positive" ~ 1,
      TRUE ~ 0
    ),
    malaria = case_when(
      malaria == "pf_recent" ~ "Recent P. Falciparum",
      malaria == "pf_historic" ~ "Historical P. Falciparum",
      malaria == "pv_recent" ~ "Recent P. Vivax",
      malaria == "pv_historic" ~ "Historical P. Vivax"
    ),
    across(
      c(ffi_is_community:ffi_is_health_facility_name),
      str_to_title
    )
  ) %>%
  group_by(ffi_is_fever_month, age_cat, malaria) %>%
  summarise(result_malaria = mean(result_malaria))

## `summarise()` has grouped output by 'ffi_is_fever_month', 'age_cat'. You can
## override using the `.groups` argument.

sero_4malaria_fever_month <- ffi_fever_month %>%
  ggplot(
    aes(
      x = result_malaria,
      y = age_cat,
      group = age_cat
    )
  ) +
  geom_path(
    color = "#bdbdbd"
  ) +
  geom_point(
    aes(color = malaria),
    size = 3
  ) +
  facet_wrap(vars(ffi_is_fever_month)) +
  scale_x_continuous(
    labels = scales::percent_format(),
    #limits = c(0, 0.5)
  ) +
  labs(
    y = "Age",
    x = "Seropositivity",
    title = str_wrap("Malaria seropositivity by type of exposure, plasmodium and presence of fever in the last month", 100)
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

ggsave(
  "./02_output/plots/plot16_sero_4malaria_fevermonth.png",
  sero_4malaria_fever_month,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)

ffi_fever_month_typeofmalaria <- ffi_total %>%
  drop_na(pf_recent:pv_historic, age_cat, ffi_is_fever_month) %>%
  pivot_longer(
    cols = pv_exposure:pf_exposure,
    names_to = "exposure",
    values_to = "result_exposure"
  ) %>%
    mutate(
      exposure = case_when(
        exposure == "pv_exposure" ~ "P. Vivax Exposure",
        exposure == "pf_exposure" ~ "P. Falciparum Exposure"
      ),
      result_exposure = case_when(
        result_exposure == "Positive" ~ 1,
        TRUE ~ 0
      ),
      across(
        c(ffi_is_community:ffi_is_health_facility_name),
        str_to_title
      )
    ) %>%
    group_by(ffi_is_fever_month, age_cat, exposure) %>%
    summarise(result_exposure = mean(result_exposure))

## `summarise()` has grouped output by 'ffi_is_fever_month', 'age_cat'. You can
## override using the `.groups` argument.

sero_typeofmalaria_fever_month <- ffi_fever_month_typeofmalaria %>%
  ggplot(
    aes(
      x = result_exposure,
      y = age_cat,
      group = age_cat
    )
  ) +
  geom_path(
    color = "#bdbdbd"
  ) +
  geom_point(
    aes(color = exposure),
    size = 3
  ) +
  facet_wrap(vars(ffi_is_fever_month)) +
  scale_x_continuous(
    labels = scales::percent_format(),
    # limits = c(0, 0.5)
  ) +
  labs(
    y = "Age",
    x = "Seropositivity",
    title = str_wrap("Malaria seropositivity by type of plasmodium and presence of fever in the last month", 100)
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

ggsave(
  "./02_output/plots/plot16_sero_typeofmalaria_fevermonth.png",
  sero_typeofmalaria_fever_month,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)

ffi_fever_month_timeofmalaria <- ffi_total %>%
  drop_na(pf_recent:pv_historic, age_cat, ffi_is_fever_month) %>%
  pivot_longer(
    cols = recent_exposure:historical_exposure,
    names_to = "exposure",
    values_to = "result_exposure"
  ) %>%
  mutate(
    exposure = case_when(
      exposure == "recent_exposure" ~ "Recent Exposure",
      exposure == "historical_exposure" ~ "Historical Exposure"
    ),
    result_exposure = case_when(
      result_exposure == "Positive" ~ 1,
      TRUE ~ 0
    ),
    across(
      c(ffi_is_community:ffi_is_health_facility_name),
      str_to_title
    )
  ) %>%
  group_by(ffi_is_fever_month, age_cat, exposure) %>%
  summarise(result_exposure = mean(result_exposure))

## `summarise()` has grouped output by 'ffi_is_fever_month', 'age_cat'. You can
## override using the `.groups` argument.

sero_timeofmalaria_fever_month <- ffi_fever_month_typeofmalaria %>%
  ggplot(
    aes(
      x = result_exposure,
      y = age_cat,
      group = age_cat
    )
  ) +
  geom_path(
    color = "#bdbdbd"
  ) +
  geom_point(
    aes(color = exposure),
    size = 3
  ) +
  facet_wrap(vars(ffi_is_fever_month)) +
  scale_x_continuous(
    labels = scales::percent_format(),
    # limits = c(0, 0.5)
  ) +
  labs(
    y = "Age",
    x = "Seropositivity",
    title = str_wrap("Malaria seropositivity by time of plasmodium and presence of fever in the last month", 100)
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

ggsave(
  "./02_output/plots/plot16_sero_timeofmalaria_fevermonth.png",
  sero_timeofmalaria_fever_month,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)

sero_4malaria_district_antimaldrugs <- seropositivy_summarise(
  ffi_total,
  "4malaria",
  age_cat,
  ffi_is_antimal_drug_use
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_malaria,
      color = malaria,
      group = malaria
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(ffi_is_antimal_drug_use),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    limits = c(0, 0.50)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

## `summarise()` has grouped output by 'ffi_is_district', 'age_cat',
## 'ffi_is_antimal_drug_use'. You can override using the `.groups` argument.

sero_4malaria_district_antimaldrugs

ggsave(
  "./02_output/plots/plot19_sero_4malaria_district_antimaldrugs.png",
  sero_4malaria_district_antimaldrugs,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)

sero_typeofmalaria_district_antimaldrugs <- seropositivy_summarise(
  ffi_total,
  "typeofmalaria",
  age_cat,
  ffi_is_antimal_drug_use
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_exposure,
      color = exposure,
      group = exposure
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(ffi_is_antimal_drug_use),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    limits = c(0, 0.60)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

## `summarise()` has grouped output by 'ffi_is_district', 'age_cat',
## 'ffi_is_antimal_drug_use'. You can override using the `.groups` argument.

sero_typeofmalaria_district_antimaldrugs

ggsave(
  "./02_output/plots/plot20_sero_typeofmalaria_district_antimaldrugs.png",
  sero_typeofmalaria_district_antimaldrugs,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)

sero_timeofmalaria_district_antimaldrugs <- seropositivy_summarise(
  ffi_total,
  "timeofmalaria",
  age_cat,
  ffi_is_antimal_drug_use
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_exposure,
      color = exposure,
      group = exposure
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(ffi_is_antimal_drug_use),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    limits = c(0, 0.5)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

## `summarise()` has grouped output by 'ffi_is_district', 'age_cat',
## 'ffi_is_antimal_drug_use'. You can override using the `.groups` argument.

sero_timeofmalaria_district_antimaldrugs

ggsave(
  "./02_output/plots/plot21_sero_timeofmalaria_district_antimaldrugs.png",
  sero_timeofmalaria_district_antimaldrugs,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)

ffi_4malaria_mal_lifetime <- ffi_total %>% 
  drop_na(only_pv_exposure:freedom_malaria, 
          age_code, ffi_is_mal_lifetime) %>% 
  pivot_longer(
    cols = only_pv_exposure:freedom_malaria,
    names_to = "malaria",
    values_to = "result_malaria"
  ) %>% 
  mutate(
    result_malaria = case_when(
      result_malaria == "Positive" ~ 1,
      TRUE ~ 0
    ),
    malaria = case_when(
      malaria == "only_pv_exposure" ~ "Only P. vivax",
      malaria == "only_pf_exposure" ~ "Only P. falciparum",
      malaria == "pv_pf_exposure" ~ "P. vivax & falciparum Exposure",
      malaria == "freedom_malaria" ~ "Freedom from Malaria"
    ),
    across(
      c(ffi_is_community:ffi_is_health_facility_name),
      str_to_title
    )
  ) %>%
  group_by(ffi_is_mal_lifetime, age_code, malaria) %>%
  summarise(result_malaria = mean(result_malaria))

## `summarise()` has grouped output by 'ffi_is_mal_lifetime', 'age_code'. You can
## override using the `.groups` argument.

sero_4malaria_mal_lifetime <- ffi_4malaria_mal_lifetime %>%
  ggplot(
    aes(
      x = age_code,
      y = result_malaria,
      fill = malaria
    )
  ) +
  geom_area() +
  scale_x_continuous(
    limits = c(1, 8),
    breaks = seq(1, 8, 1),
    labels = c(
      "[0-10)",
      "[10-20)",
      "[20-30)",
      "[30-40)",
      "[40-50)",
      "[50-60)",
      "[60-70)",
      "[70+)"
    ),
    expand = c(0, 0)
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    expand = c(0, 0)
  ) +
  facet_wrap(vars(ffi_is_mal_lifetime)) +
  labs(
    y = "Seropositivity",
    x = "Age",
    title = str_wrap("Malaria seropositivity by type of exposure, plasmodium and how many times they think they have had malaria", 100)
  ) +
  guides(
    fill = guide_legend("Malaria")
  ) +
  innovar::scale_fill_innova("npr") +
  theme_bw() +
  theme(
    axis.text.x = element_text(
      angle = 45,
      vjust = 1,
      hjust = 1
    ),
    panel.spacing = unit(1, "lines")
  )

sero_4malaria_mal_lifetime

ggsave(
  "./02_output/plots/plot22_sero_4ofmalaria_mal_lifetime.png",
  sero_4malaria_mal_lifetime,
  dpi = 300,
  bg = "white",
  width = 11,
  height = 5
)

ffi_recentmalaria_mal_lifetime <- ffi_total %>% 
  drop_na(only_pv_recent:freedom_malaria_recent, 
          age_code, ffi_is_mal_lifetime) %>% 
  pivot_longer(
    cols = only_pv_recent:freedom_malaria_recent,
    names_to = "malaria",
    values_to = "result_malaria"
  ) %>% 
  mutate(
    result_malaria = case_when(
      result_malaria == "Positive" ~ 1,
      TRUE ~ 0
    ),
    malaria = case_when(
      malaria == "only_pv_recent" ~ "Only P. vivax Recent",
      malaria == "only_pf_recent" ~ "Only P. falciparum Recent",
      malaria == "pv_pf_recent" ~ "P. vivax & falciparum Recent",
      malaria == "freedom_malaria_recent" ~ "Freedom from Malaria Recent"
    ),
    across(
      c(ffi_is_community:ffi_is_health_facility_name),
      str_to_title
    )
  ) %>%
  group_by(ffi_is_mal_lifetime, age_code, malaria) %>%
  summarise(result_malaria = mean(result_malaria))

## `summarise()` has grouped output by 'ffi_is_mal_lifetime', 'age_code'. You can
## override using the `.groups` argument.

sero_recentmalaria_mal_lifetime <- ffi_recentmalaria_mal_lifetime %>%
  ggplot(
    aes(
      x = age_code,
      y = result_malaria,
      fill = malaria
    )
  ) +
  geom_area() +
  scale_x_continuous(
    limits = c(1, 8),
    breaks = seq(1, 8, 1),
    labels = c(
      "[0-10)",
      "[10-20)",
      "[20-30)",
      "[30-40)",
      "[40-50)",
      "[50-60)",
      "[60-70)",
      "[70+)"
    ),
    expand = c(0, 0)
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    expand = c(0, 0)
  ) +
  facet_wrap(vars(ffi_is_mal_lifetime)) +
  labs(
    y = "Seropositivity",
    x = "Age",
    title = str_wrap("Malaria seropositivity by recent exposure and how many times they think they have had malaria", 100)
  ) +
  guides(
    fill = guide_legend("Malaria")
  ) +
  innovar::scale_fill_innova("npr") +
  theme_bw() +
  theme(
    axis.text.x = element_text(
      angle = 45,
      vjust = 1,
      hjust = 1
    ),
    panel.spacing = unit(1, "lines")
  )

sero_recentmalaria_mal_lifetime

ggsave(
  "./02_output/plots/plot23_sero_recent_malaria_mal_lifetime.png",
  sero_recentmalaria_mal_lifetime,
  dpi = 300,
  bg = "white",
  width = 11,
  height = 5
)

ffi_historicmalaria_mal_lifetime <- ffi_total %>% 
  drop_na(only_pv_historic:freedom_malaria_historic, 
          age_code, ffi_is_mal_lifetime) %>% 
  pivot_longer(
    cols = only_pv_historic:freedom_malaria_historic,
    names_to = "malaria",
    values_to = "result_malaria"
  ) %>% 
  mutate(
    result_malaria = case_when(
      result_malaria == "Positive" ~ 1,
      TRUE ~ 0
    ),
    malaria = case_when(
      malaria == "only_pv_historic" ~ "Only P. vivax Historic",
      malaria == "only_pf_historic" ~ "Only P. falciparum Historic",
      malaria == "pv_pf_historic" ~ "P. vivax & falciparum Historic",
      malaria == "freedom_malaria_historic" ~ "Freedom from Malaria Historic"
    ),
    across(
      c(ffi_is_community:ffi_is_health_facility_name),
      str_to_title
    )
  ) %>%
  group_by(ffi_is_mal_lifetime, age_code, malaria) %>%
  summarise(result_malaria = mean(result_malaria))

## `summarise()` has grouped output by 'ffi_is_mal_lifetime', 'age_code'. You can
## override using the `.groups` argument.

sero_historicmalaria_mal_lifetime <- ffi_historicmalaria_mal_lifetime %>%
  ggplot(
    aes(
      x = age_code,
      y = result_malaria,
      fill = malaria
    )
  ) +
  geom_area() +
  scale_x_continuous(
    limits = c(1, 8),
    breaks = seq(1, 8, 1),
    labels = c(
      "[0-10)",
      "[10-20)",
      "[20-30)",
      "[30-40)",
      "[40-50)",
      "[50-60)",
      "[60-70)",
      "[70+)"
    ),
    expand = c(0, 0)
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    expand = c(0, 0)
  ) +
  facet_wrap(vars(ffi_is_mal_lifetime)) +
  labs(
    y = "Seropositivity",
    x = "Age",
    title = str_wrap("Malaria seropositivity by historic exposure and how many times they think they have had malaria", 100)
  ) +
  guides(
    fill = guide_legend("Malaria")
  ) +
  innovar::scale_fill_innova("npr") +
  theme_bw() +
  theme(
    axis.text.x = element_text(
      angle = 45,
      vjust = 1,
      hjust = 1
    ),
    panel.spacing = unit(1, "lines")
  )

sero_historicmalaria_mal_lifetime

ggsave(
  "./02_output/plots/plot24_sero_historic_malaria_mal_lifetime.png",
  sero_historicmalaria_mal_lifetime,
  dpi = 300,
  bg = "white",
  width = 11,
  height = 5
)

sero_4malaria_district_ussualybathe<- seropositivy_summarise(
  ffi_total,
  "4malaria",
  age_cat,
  ffi_is_place_shower
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_malaria,
      color = malaria,
      group = malaria
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(ffi_is_place_shower),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    #limits = c(0, 0.50)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

## `summarise()` has grouped output by 'ffi_is_district', 'age_cat',
## 'ffi_is_place_shower'. You can override using the `.groups` argument.

sero_4malaria_district_ussualybathe

ggsave(
  "./02_output/plots/plot25_sero_4malaria_district_ussualybathe.png",
  sero_4malaria_district_ussualybathe,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)

sero_typeofmalaria_district_ussualybathe <- seropositivy_summarise(
  ffi_total,
  "typeofmalaria",
  age_cat,
  ffi_is_place_shower
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_exposure,
      color = exposure,
      group = exposure
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(ffi_is_place_shower),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    #limits = c(0, 0.60)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

## `summarise()` has grouped output by 'ffi_is_district', 'age_cat',
## 'ffi_is_place_shower'. You can override using the `.groups` argument.

sero_typeofmalaria_district_ussualybathe

ggsave(
  "./02_output/plots/plot26_sero_typeofmalaria_district_ussualybathe.png",
  sero_typeofmalaria_district_ussualybathe,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)

sero_timeofmalaria_district_ussualybathe <- seropositivy_summarise(
  ffi_total,
  "timeofmalaria",
  age_cat,
  ffi_is_place_shower
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_exposure,
      color = exposure,
      group = exposure
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(ffi_is_place_shower),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    #limits = c(0, 0.5)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

## `summarise()` has grouped output by 'ffi_is_district', 'age_cat',
## 'ffi_is_place_shower'. You can override using the `.groups` argument.

sero_timeofmalaria_district_ussualybathe

ggsave(
  "./02_output/plots/plot27_sero_timeofmalaria_district_ussualybathe.png",
  sero_timeofmalaria_district_ussualybathe,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)

sero_4malaria_district_mosquitnet <- seropositivy_summarise(
  ffi_total,
  "4malaria",
  age_cat,
  ffi_is_mosq_net
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_malaria,
      color = malaria,
      group = malaria
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(ffi_is_mosq_net),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    #limits = c(0, 0.50)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

## `summarise()` has grouped output by 'ffi_is_district', 'age_cat',
## 'ffi_is_mosq_net'. You can override using the `.groups` argument.

sero_4malaria_district_mosquitnet

ggsave(
  "./02_output/plots/plot28_sero_4malaria_district_mosquitnet.png",
  sero_4malaria_district_mosquitnet,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)

sero_typeofmalaria_district_mosquitnet <- seropositivy_summarise(
  ffi_total,
  "typeofmalaria",
  age_cat,
  ffi_is_mosq_net
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_exposure,
      color = exposure,
      group = exposure
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(ffi_is_mosq_net),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    #limits = c(0, 0.60)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

## `summarise()` has grouped output by 'ffi_is_district', 'age_cat',
## 'ffi_is_mosq_net'. You can override using the `.groups` argument.

sero_typeofmalaria_district_mosquitnet

ggsave(
  "./02_output/plots/plot29_sero_typeofmalaria_district_mosquitnet.png",
  sero_typeofmalaria_district_mosquitnet,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)

sero_timeofmalaria_district_mosquitnet <- seropositivy_summarise(
  ffi_total,
  "timeofmalaria",
  age_cat,
  ffi_is_mosq_net
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_exposure,
      color = exposure,
      group = exposure
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(ffi_is_mosq_net),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    #limits = c(0, 0.5)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

## `summarise()` has grouped output by 'ffi_is_district', 'age_cat',
## 'ffi_is_mosq_net'. You can override using the `.groups` argument.

sero_timeofmalaria_district_mosquitnet

ggsave(
  "./02_output/plots/plot30_sero_timeofmalaria_district_mosquitnet.png",
  sero_timeofmalaria_district_mosquitnet,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)

sero_4malaria_district_tripmonth <- seropositivy_summarise(
  ffi_total,
  "4malaria",
  age_cat,
  ffi_is_trip_month
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_malaria,
      color = malaria,
      group = malaria
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(ffi_is_trip_month),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    #limits = c(0, 0.50)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

## `summarise()` has grouped output by 'ffi_is_district', 'age_cat',
## 'ffi_is_trip_month'. You can override using the `.groups` argument.

sero_4malaria_district_tripmonth

ggsave(
  "./02_output/plots/plot31_sero_4malaria_district_tripmonth.png",
  sero_4malaria_district_tripmonth,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)

sero_typeofmalaria_district_tripmonth <- seropositivy_summarise(
  ffi_total,
  "typeofmalaria",
  age_cat,
  ffi_is_trip_month
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_exposure,
      color = exposure,
      group = exposure
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(ffi_is_trip_month),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    #limits = c(0, 0.60)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

## `summarise()` has grouped output by 'ffi_is_district', 'age_cat',
## 'ffi_is_trip_month'. You can override using the `.groups` argument.

sero_typeofmalaria_district_tripmonth

ggsave(
  "./02_output/plots/plot32_sero_typeofmalaria_district_tripmonth.png",
  sero_typeofmalaria_district_tripmonth,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)

sero_timeofmalaria_district_tripmonth <- seropositivy_summarise(
  ffi_total,
  "timeofmalaria",
  age_cat,
  ffi_is_trip_month
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_exposure,
      color = exposure,
      group = exposure
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(ffi_is_trip_month),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    #limits = c(0, 0.5)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

## `summarise()` has grouped output by 'ffi_is_district', 'age_cat',
## 'ffi_is_trip_month'. You can override using the `.groups` argument.

sero_timeofmalaria_district_tripmonth

ggsave(
  "./02_output/plots/plot33_sero_timeofmalaria_district_tripmonth.png",
  sero_timeofmalaria_district_tripmonth,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)

plot_4_2 <- ffi_total %>%
  filter(ffi_is_malaria %in% c("No", "Yes")) %>%
  drop_na(age_cat, pv_historic) %>%
  mutate(
    malaria_gender = paste(gender, ffi_is_malaria)
  ) %>%
  count(pv_historic, age_cat, malaria_gender) %>%
  mutate(Percentage = n / sum(n)) %>%
  mutate(
    Percentage = case_when(
      str_detect(malaria_gender, "Female") ~ Percentage * -1,
      TRUE ~ Percentage
    )
  ) %>%
  ggplot(aes(
    y = age_cat,
    x = Percentage,
    fill = malaria_gender
  )) +
  geom_col(
    position = position_stack(reverse = TRUE),
    color = "black"
  ) +
  labs(
    x = "Age Group",
    y = NULL,
    title = "Population structure by age groups, gender and malaria transmission"
  ) +
  facet_wrap(vars(pv_historic)) + 
  scale_x_continuous(
    limits = c(-0.25, 0.25),
    breaks = seq(-0.25, 0.25, 0.05),
    labels = c(paste0(seq(25, 0, -5), "%"), paste0(seq(5, 25, 5), "%"))
  ) +
  # innovar::scale_fill_innova("npr") +
  scale_fill_discrete(
    type = innovar::innova_pal("npr")(4),
    limits = c("Male Yes", "Male No", "Female No", "Female Yes")
  ) +
  guides(
    fill = guide_legend("Have you ever \nhad malaria?")
  ) +
  geom_vline(xintercept = 0, size = 1) +
  theme_bw(base_size = 12) +
  theme(
    plot.title = element_text(
      size = 14,
      hjust = 0.5,
      face = "bold"
    ),
    strip.text = element_text(
      size = 12,
      face = "bold"
    ),
    legend.title = element_text(
      size = 12,
      face = "bold"
    ),
    plot.margin = margin(5, 15, 5, 15)
  )

plot_4_2 <- ffi_total %>%
  filter(ffi_is_malaria %in% c("No", "Yes")) %>%
  drop_na(age_cat, pv_historic) %>%
  mutate(
    malaria_gender = paste(gender, ffi_is_malaria)
  ) %>%
  count(pv_historic, age_cat, malaria_gender) %>%
  mutate(Percentage = n / sum(n)) %>%
  mutate(
    Percentage = case_when(
      str_detect(malaria_gender, "Female") ~ Percentage * -1,
      TRUE ~ Percentage
    )
  ) %>%
  ggplot(aes(
    y = age_cat,
    x = Percentage,
    fill = malaria_gender
  )) +
  geom_col(
    position = position_stack(reverse = TRUE),
    color = "black"
  ) +
  labs(
    x = "Age Group",
    y = NULL,
    title = "Population structure by age groups, gender and malaria transmission"
  ) +
  facet_wrap(vars(pv_historic)) + 
  scale_x_continuous(
    limits = c(-0.25, 0.25),
    breaks = seq(-0.25, 0.25, 0.05),
    labels = c(paste0(seq(25, 0, -5), "%"), paste0(seq(5, 25, 5), "%"))
  ) +
  # innovar::scale_fill_innova("npr") +
  scale_fill_discrete(
    type = innovar::innova_pal("npr")(4),
    limits = c("Male Yes", "Male No", "Female No", "Female Yes")
  ) +
  guides(
    fill = guide_legend("Have you ever \nhad malaria?")
  ) +
  geom_vline(xintercept = 0, size = 1) +
  theme_bw(base_size = 12) +
  theme(
    plot.title = element_text(
      size = 14,
      hjust = 0.5,
      face = "bold"
    ),
    strip.text = element_text(
      size = 12,
      face = "bold"
    ),
    legend.title = element_text(
      size = 12,
      face = "bold"
    ),
    plot.margin = margin(5, 15, 5, 15)
  )

library(ggsankey)

ffi_format_sankey <- ffi_total %>%
  mutate(
    ffi_is_access_malaria_yn_hf = factor(
      ffi_is_access_malaria_yn_hf,
      label = c("No", "Yes")
    ),
    ffi_is_mal_lifetime = as.character(ffi_is_mal_lifetime),
    ffi_is_mal_lifetime = replace_na(ffi_is_mal_lifetime, "0 times"),
    ffi_is_mal_lifetime = factor(
      ffi_is_mal_lifetime,
      labels = c(
        "0 times",
        "1 to 3 times",
        "3 to 7 times",
        "More than 7 times"
      )
    )
  )

sankey_seropositive_answ1 <- ffi_format_sankey %>%
  pivot_longer(
    cols = c(recent_exposure, ffi_is_mal_lifetime, ffi_is_access_malaria_yn_hf),
    names_to = "malaria_behavior1",
    values_to = "malaria_answ1"
  ) %>%
  count(malaria_behavior1, malaria_answ1) %>%
  group_by(malaria_behavior1) %>%
  mutate(
    percentage = scales::percent(
      n / sum(n),
      accuracy = 0.1
    )
  ) %>%
  ungroup() %>%
  mutate(
    malaria_behavior1 = fct_relevel(
      malaria_behavior1,
      "ffi_is_mal_lifetime",
      "ffi_is_access_malaria_yn_hf",
      "recent_exposure"
    ),
    malaria_percent1 = paste0(
      malaria_answ1,
      paste0("\n(", percentage, ")")
    ),
    malaria_percent1 = as_factor(malaria_percent1)
  ) %>%
  select(malaria_behavior1, malaria_answ1, malaria_percent1)


sankey_seropositive <- ffi_format_sankey %>%
  drop_na(ffi_is_mal_lifetime, 
  ffi_is_access_malaria_yn_hf,
  recent_exposure
  ) %>%
  make_long(
    ffi_is_mal_lifetime,
    ffi_is_access_malaria_yn_hf, 
    recent_exposure
  ) %>%
  left_join(
    sankey_seropositive_answ1,
      by = c(
        "x" = "malaria_behavior1",
        "node" = "malaria_answ1"
      )
  ) %>%
  mutate(
    node = malaria_percent1,
    node = fct_rev(node)
  ) %>%
  select(-malaria_percent1) %>%
  left_join(
    sankey_seropositive_answ1,
    by = c(
      "next_x" = "malaria_behavior1",
      "next_node" = "malaria_answ1"
    )
  ) %>%
  mutate(next_node = malaria_percent1) %>%
  select(-malaria_percent1) %>%
  ggplot(aes(
    x = x,
    next_x = next_x,
    node = node,
    next_node = next_node,
    fill = factor(node),
    label = node
  )) +
  geom_sankey(
    flow.alpha = .8,
    node.color = "gray30"
  ) +
  geom_sankey_label(size = 4, color = "white", fill = "gray30") +
  scale_x_discrete(
    labels = str_wrap(
      c(
        "How many times do you think you have had malaria in your life?",
        "If you had malaria, would you go to the health facility?",
        "Recent exposure to any type of plasmodium"
      ),
      30
    )
  ) +
  theme_sankey(base_size = 18) +
  innovar::scale_fill_innova("npr") +
  labs(
    x = NULL,
    title = "Behaviour associated with recent malaria exposure"
  ) +
  theme(
    legend.position = "none",
    plot.title = element_text(hjust = .5),
    plot.margin = margin(5, 5, 5, 5)
  )

## Warning: attributes are not identical across measure variables;
## they will be dropped

ggsave("./02_output/plots/plot34_sankey_seropositive.png",
       sankey_seropositive,
       height = 7,
       width = 14,
       dpi = 300)

sero_4malaria_district_gender_economic <- seropositivy_summarise(
  ffi_total,
  "4malaria",
  economic_activities,
  gender
) %>%
  ggplot(
    aes(
      x = economic_activities,
      y = result_malaria,
      color = malaria,
      group = malaria
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(gender),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    limits = c(0, 0.40)
  ) +
  labs(
    x = "Economic Activities",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw() +
  theme(
    axis.text.x = element_text(
      angle = 45,
      vjust = 1,
      hjust = 1
    )
  )

## `summarise()` has grouped output by 'ffi_is_district', 'economic_activities',
## 'gender'. You can override using the `.groups` argument.

sero_4malaria_district_gender_economic

ggsave(
  "./02_output/plots/plot35_sero_4malaria_district_gender_economic.png",
  sero_4malaria_district_gender_economic,
  dpi = 300,
  bg = "white",
  width = 11,
  height = 7.5,
  scale = 0.9
)

sero_typeofmalaria_district_gender <- seropositivy_summarise(
  ffi_total,
  "typeofmalaria",
  age_cat,
  gender
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_exposure,
      color = exposure,
      group = exposure
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(gender),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    limits = c(0, 0.45)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

## `summarise()` has grouped output by 'ffi_is_district', 'age_cat', 'gender'. You
## can override using the `.groups` argument.

sero_typeofmalaria_district_gender

ggsave(
  "./02_output/plots/plot11_sero_typeofmalaria_district_gender.png",
  sero_typeofmalaria_district_gender,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)

sero_timeofmalaria_district_gender <- seropositivy_summarise(
  ffi_total,
  "timeofmalaria",
  age_cat,
  gender
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_exposure,
      color = exposure,
      group = exposure
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(gender),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    limits = c(0, 0.45)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

## `summarise()` has grouped output by 'ffi_is_district', 'age_cat', 'gender'. You
## can override using the `.groups` argument.

sero_timeofmalaria_district_gender

ggsave(
  "./02_output/plots/plot12_sero_timeofmalaria_district_gender.png",
  sero_timeofmalaria_district_gender,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)

library(sf)
library(leaflet)

ffi_total_gps <- ffi_total %>%
  select(
    ffi_is_cod_com:ffi_is_cod_ind,
    pf_recent:pv_historic,
    pv_exposure:historical_exposure
  ) %>%
  left_join(
    ffi_household %>%
      select(
        ffi_h_code_community:ffi_h_code_household,
        ffi_h_community
      ),
    by = c(
      "ffi_is_cod_com" = "ffi_h_code_community",
      "ffi_is_cod_household" = "ffi_h_code_household"
    )
  ) 

ffi_household_gps <- ffi_total_gps %>%
  select(
    ffi_is_cod_com:ffi_is_cod_household,
    ffi_h_community,
    recent_exposure
  ) %>%
  mutate(
    recent_exposure = case_when(
      recent_exposure == "Positive" ~ 1,
      TRUE ~ 0
    )
  ) %>%
  group_by(
    across(c(ffi_is_cod_com:ffi_h_community))
  ) %>%
  summarise(
    recent_exposure = mean(recent_exposure)
  ) %>%
  ungroup()

## `summarise()` has grouped output by 'ffi_is_cod_com', 'ffi_is_cod_household'.
## You can override using the `.groups` argument.

  # mutate(
  #   color_marker = case_when(
  #     recent_exposure == 0 ~ "red",
  #     TRUE ~ "black"
  #   )
  # )

ffi_household_gps <- ffi_household_gps %>%
  left_join(
    ffi_household %>%
      select(
        ffi_h_code_community:ffi_h_code_household,
        ffi_h_district,
        ffi_gps_long,
        ffi_gps_lat
      ),
    by = c(
      "ffi_is_cod_com" = "ffi_h_code_community",
      "ffi_is_cod_household" = "ffi_h_code_household"
    )
  ) %>%
  mutate(
    ffi_h_household_id = paste0(ffi_is_cod_com, ffi_is_cod_household),
    ffi_is_cod_com_label = paste0(
      "Cod: ",
      ffi_is_cod_household,
      " - ",
      str_to_title(ffi_h_community),
      " (",
      str_to_title(ffi_h_district),
      ")"
    )
  ) %>%
  st_as_sf(
    coords = c("ffi_gps_long", "ffi_gps_lat"),
    crs = 4326
  ) 
  

  
# colors_pal <- rev(innovar:::innova_palettes[["ecomst"]])
# pal <- colorNumeric(
#   colorRamp(colors_pal),
#   ffi_household_gps$recent_exposure
# )

pal <- colorNumeric(
  hcl.colors(17, palette = "zissou"),
  ffi_household_gps$recent_exposure
)

# icons_household <- awesomeIcons(
#   icon = "ios-home",
#   iconColor = "black",
#   library = "ion",
#   markerColor = innovar::innova_pal("ecomst", reverse = TRUE)(17)
# )

communities_sf <- communities_sf %>%
  mutate(
    ffi_h_community = str_to_title(ffi_h_community)
  ) %>%
  left_join(
    ffi_household %>%
      select(ffi_h_district, ffi_h_code_community) %>%
      distinct()
  )  %>%
  mutate(
    ffi_h_community_label = paste0(
      ffi_h_community,
      " (",
      str_to_title(ffi_h_district),
      ")"
    )
  )

## Joining, by = c("ffi_h_district", "ffi_h_code_community")

household_communities_leaft <- ffi_household_gps %>%
  leaflet() %>%
  addProviderTiles(
    providers$OpenStreetMap,
    group = "OpenStreetMap"
  ) %>%
  addCircleMarkers(
    layerId = ~ffi_h_household_id,
    label = ~ffi_is_cod_com_label,
    color = ~ pal(recent_exposure),
    group = "Households",
    radius = 7,
    weight = 5,
    opacity = 1,
    fillOpacity = 0.1,
    labelOptions = labelOptions(
      style = list(
        "font-weight" = "bold",
        padding = "3px 8px"
      ),
      textsize = "12px",
      direction = "auto"
    )
  ) %>%
  # addLegend(
  #   title = "Recent Exposure",
  #   pal = pal, 
  #   values = ~ recent_exposure, 
  #   group = "circles", 
  #   position = "bottomleft",
  #   transform = ~ scales::percent_format(),
  #   opacity = 1
  # ) %>%<font-awesome-icon icon="fa-solid fa-location-dot" />
  leaflegend::addLegendNumeric(
    title = "Recent Exposure",
    pal = pal,
    values = ~ recent_exposure,
    position = "bottomright",
    orientation = "horizontal",
    height = 20,
    width = 100,
    decreasing = FALSE,
    numberFormat = scales::percent_format(),
    group = "circles"
  )  %>%
  addAwesomeMarkers(
    data = communities_sf,
    layerId = ~ffi_h_code_community,
    label = ~ffi_h_community_label,
    group = "Communities"
  ) %>%
  addProviderTiles(
    providers$OpenStreetMap,
    group = "OpenStreetMap"
  ) %>%
  addTiles(
    urlTemplate = "http://mt0.google.com/vt/lyrs=m&hl=en&x={x}&y={y}&z={z}&s=Ga",
    attribution = "Google Maps",
    options = leaflet::tileOptions(
      maxNativeZoom = 19,
      maxZoom = 20
    ),
    group = "Google Maps"
  ) %>%
  addTiles(
    urlTemplate = "https://mt1.google.com/vt/lyrs=s&x={x}&y={y}&z={z}",
    attribution = "Satellite View",
    options = leaflet::tileOptions(
      maxNativeZoom = 19,
      maxZoom = 20
    ),
    group = "Satellite View"
  ) %>%
  # Layers control*
  addLayersControl(
    overlayGroups = c("Households", "Communities"),
    baseGroups = c("OpenStreetMap", "Google Maps", "Satellite View"),
    options = layersControlOptions(collapsed = FALSE)
  ) %>%
  # addLegend(
  #   "topright",
  #   pal = innovar::innova_pal("ecomst", reverse = TRUE)(980)
  # )  %>%
  # addLegend(
  #   position = "bottomright",
  #   colors = c(
  #     "white; width:15px; height:15px;
  #                border:5px solid red; border-radius:50%;",
  #     "white; width:7.5px; height:7.5px; margin-top: 5px;
  #                border:5px solid black; border-radius:50%; margin-left:2px;"
  #   ),
  #   labels = c(
  #     "<div style='display: inline-block; height: 10px;
  #                margin-top: 8px;line-height: 10px;font-weight: bold;
  #                color: black; '>At least 1 person with recent malaria</div>",
  #     "<div style='display: inline-block;height: 10px;
  #                margin-top: 8px;line-height: 10px;font-weight: bold;
  #                color: black; margin-top: 10px;
  #                margin-left:5px'>No one with recent malaria at household</div>"
  #   ),
  #   opacity = 1
  # ) %>%
  leaflet.extras::addResetMapButton()

htmlwidgets::saveWidget(
  household_communities_leaft,
  file = "./02_output/plots/household_communities_leaft.html"
)

webshot::webshot(
  "./02_output/plots/household_communities_leaft.html",
  "./02_output/plots/household_communities_leaft.png",
  cliprect = "viewport",
  zoom = 4
)

---
title: "Descriptive analysis"
date: "`r Sys.Date()`"
output: 
  rmdformats::downcute:
    self_contained: true
    highlight: kate
    toc_depth: 3
    default_style: dark
    code_folding: hide
    code_download: true
    highlight_downlit: true
editor_options: 
  chunk_output_type: console
---

```{r setup, include=FALSE}
## Global options
knitr::opts_chunk$set(
  cache = TRUE,
  warnings = FALSE,
  messages = FALSE
)

library(tidyverse)
library(sf)
library(ggsflabel)
```

# Import data

```{r}
serology <- read_csv("./01_data/raw/FFI.peru.serology.kmeans2.csv") %>%
  rename(ffi_is_code = Codigo) %>%
  select(-1)

ffi_individual <- read_csv("./01_data/raw/individuals_result_share_072022.csv")
ffi_household <- read_csv("./01_data/raw/household_gps_share_072022.csv")
data_2000_2019 <- read_csv("./01_data/raw/Data_FFI_2000_2019_20212405.csv")
data_2020_2021 <- read_csv("./01_data/raw/Data_FFI_2020_2021_20210629.csv")
poblacion <- readRDS("./01_data/raw/poblacion_inei_2017.rds")
```

# Format data

```{r}
ffi_total <- ffi_individual %>%
  full_join(
    serology %>%
      mutate(
        ffi_is_code = paste0(0, ffi_is_code)
      ),
    by = "ffi_is_code"
  )

ffi_total <- ffi_total %>%
  mutate(
    age_cat = case_when(
      ffi_is_age_fixed >= 70 ~ "[70+)",
      ffi_is_age_fixed >= 60 ~ "[60-70)",
      ffi_is_age_fixed >= 50 ~ "[50-60)",
      ffi_is_age_fixed >= 40 ~ "[40-50)",
      ffi_is_age_fixed >= 30 ~ "[30-40)",
      ffi_is_age_fixed >= 20 ~ "[20-30)",
      ffi_is_age_fixed >= 10 ~ "[10-20)",
      ffi_is_age_fixed >= 0 ~ "[0-10)"
    ),
    age_cat = factor(age_cat),
    age_code = case_when(
      age_cat == "[70+)" ~ 8,
      age_cat == "[60-70)" ~ 7,
      age_cat == "[50-60)" ~ 6,
      age_cat == "[40-50)" ~ 5,
      age_cat == "[30-40)" ~ 4,
      age_cat == "[20-30)" ~ 3,
      age_cat == "[10-20)" ~ 2,
      age_cat == "[0-10)" ~ 1,
    ),
    gender = factor(
      ffi_is_sex,
      labels = c("Male", "Female")
    ),
    ffi_is_malaria = factor(
      ffi_is_malaria,
      labels = c("No", "Yes", "Don't Know / No Answer")
    ),
    across(
      c(pf_recent:pv_historic),
      ~ factor(., labels = c("Negative", "Positive"))
    ),
    across(
      c(
        ffi_is_fever_month,
        ffi_is_antimal_drug_use, 
        ffi_is_mosq_net
      ),
      ~ factor(., labels = c("No", "Yes"))
    ),
    ffi_is_trip_month = factor(
      ffi_is_trip_month,
      labels = c("No", "Yes", "Don't Know/No Answer")
    ),
    ffi_is_mal_lifetime = factor(
      ffi_is_mal_lifetime,
      labels = c(
        "1 to 3 times",
        "3 to 7 times",
        "More than 7 times"
      )
    ),
    ffi_is_place_shower = factor(
      ffi_is_place_shower,
      labels = c(
        "Bathroom inside the dwelling",
        "Bathroom outside the dwelling",
        "In the countryside/river",
        "Other"
      )
    ),
    education_level = case_when(
      ffi_is_inst_level %in% 0 ~ "No schooling", 
      ffi_is_inst_level %in% 1:2 ~ "Primary school",
      ffi_is_inst_level %in% 3:4 ~ "Secondary school",
      ffi_is_inst_level %in% 5:6 ~ "Higher education"
    ),
    education_level = fct_relevel(education_level, "No schooling",
                                  "Primary school",
                                  "Secondary school"),
    economic_activities = factor(ffi_is_main_econ_act,
                                 labels = c("Day labourer",
                                            "Wood extractor",
                                            "Fisherman",
                                            "Livestock farmer",
                                            "Farmer",
                                            "Trader",
                                            "Housewife",
                                            "Student",
                                            "Motorcycle taxi driver",
                                            "None",
                                            "Other")),
    pv_exposure = case_when(
      pv_recent == "Negative" & pv_historic == "Negative" ~ "Negative",
      is.na(pv_recent) & is.na(pv_historic) ~ NA_character_,
      TRUE ~ "Positive"
    ),
    pf_exposure = case_when(
      pf_recent == "Negative" & pf_historic == "Negative" ~ "Negative",
      is.na(pf_recent) & is.na(pf_historic) ~ NA_character_,
      TRUE ~ "Positive"
    ),
    recent_exposure = case_when(
      pv_recent == "Negative" & pf_recent == "Negative" ~ "Negative",
      is.na(pv_recent) & is.na(pf_recent) ~ NA_character_,
      TRUE ~ "Positive"
    ),
    historical_exposure = case_when(
      pv_historic == "Negative" & pf_historic == "Negative" ~ "Negative",
      is.na(pv_historic) & is.na(pf_historic) ~ NA_character_,
      TRUE ~ "Positive"
    ),
    only_pv_exposure = case_when(
      pv_exposure == "Positive" & pf_exposure == "Negative" ~ "Positive", 
      TRUE ~ "Negative"
    ),
    only_pf_exposure = case_when(
      pf_exposure == "Positive" & pv_exposure == "Negative" ~ "Positive", 
      TRUE ~ "Negative"
    ),
    pv_pf_exposure = case_when(
      pv_exposure == "Positive" & pf_exposure == "Positive" ~ "Positive",
      TRUE ~ "Negative"
    ),
    freedom_malaria = case_when(
      pv_exposure == "Negative" & pf_exposure == "Negative" ~ "Positive", 
      TRUE ~ "Negative"
    ),
    only_pv_recent = case_when(
      pv_recent == "Positive" & pf_recent == "Negative" ~ "Positive", 
      TRUE ~ "Negative"
    ),
    only_pf_recent = case_when(
      pf_recent == "Positive" & pv_recent == "Negative" ~ "Positive", 
      TRUE ~ "Negative"
    ),
    pv_pf_recent = case_when(
      pv_recent == "Positive" & pf_recent == "Positive" ~ "Positive",
      TRUE ~ "Negative"
    ),
    freedom_malaria_recent = case_when(
      pv_recent == "Negative" & pf_recent == "Negative" ~ "Positive", 
      TRUE ~ "Negative"
    ),
    only_pv_historic = case_when(
      pv_historic == "Positive" & pf_historic == "Negative" ~ "Positive", 
      TRUE ~ "Negative"
    ),
    only_pf_historic = case_when(
      pf_historic == "Positive" & pv_historic == "Negative" ~ "Positive", 
      TRUE ~ "Negative"
    ),
    pv_pf_historic = case_when(
      pv_historic == "Positive" & pf_historic == "Positive" ~ "Positive",
      TRUE ~ "Negative"
    ),
    freedom_malaria_historic = case_when(
      pv_historic == "Negative" & pf_historic == "Negative" ~ "Positive", 
      TRUE ~ "Negative"
    ),
    across(c(pv_exposure:freedom_malaria_historic), factor)
  )

labelled::var_label(ffi_total) <- list(
  ffi_is_district = "Districs",
  gender = "Gender",
  age_cat = "Age",
  education_level = "Education Level",
  economic_activities = "Economic Activities",
  pf_recent = "Recent P. Falciparum",
  pf_historic = "Historical P. Falciparum",
  pv_recent = "Recent P. Vivax",
  pv_historic = "Historical P. Vivax",
  pv_exposure = "P. Vivax Exposure",
  pf_exposure = "P. Falciparum Exposure"
)
```

```{r eval = FALSE}
saveRDS(ffi_total, 
        file = "01_data/processed/ffi_total.rds")
```

# Table descriptive

```{r}
library(gtsummary)

fisher.test.simulate.p.values <- function(data, variable, by, ...) {
  result <- list()
  test_results <- stats::fisher.test(data[[variable]], data[[by]], simulate.p.value = TRUE)
  result$p <- test_results$p.value
  result$test <- test_results$method
  result
}
```

## Table for district

```{r}
table_1 <- ffi_total %>%
  select(
    ffi_is_district,
    gender,
    age_cat,
    education_level,
    economic_activities,
    pf_recent:pv_historic
  ) %>%
  tbl_summary(
    by = "ffi_is_district",
    missing_text = "Missing"
  ) %>%
  add_n() %>%
  add_overall() %>%
  add_p(
     test = list(all_categorical() ~ "fisher.test.simulate.p.values")
  ) %>% 
  bold_p() %>%
  modify_header(label = "**Variable**") %>%
  bold_labels()
  
table1
```

```{r eval=FALSE}
table_1 %>%
  as_flex_table() %>%
  flextable::save_as_docx(path = "./02_output/reports/table1.docx")
```

## Table for Plasmodium
```{r}
 table2 <- ffi_total %>%
   select(
     gender,
     age_cat,
     education_level,
     economic_activities,
     pf_exposure,
     pv_exposure
   ) %>%
   drop_na(pf_exposure, pv_exposure) %>%
   pivot_longer(
     cols = pv_exposure:pf_exposure,
     names_to = "exposure",
     values_to = "result_exposure"
   ) %>%
   mutate(
    exposure = case_when(
      exposure == "pv_exposure" ~ "P. Vivax Exposure",
      exposure == "pf_exposure" ~ "P. Falciparum Exposure"
    )
   ) %>%
   tbl_strata(
    strata = exposure,
    .tbl_fun =
      ~ .x %>%
          tbl_summary(
            by = result_exposure,
            missing_text = "Missing"
          ) %>%
          add_p(
            test = list(all_categorical() ~ "fisher.test.simulate.p.values")
          ) %>%
          bold_p() %>%
          modify_header(label = "**Variable**") %>%
          bold_labels() 
   ) 

table2_overall <- ffi_total %>%
  select(
    gender,
    age_cat,
    education_level,
    economic_activities,
    pf_exposure,
    pv_exposure
  ) %>%
  drop_na(pf_exposure, pv_exposure) %>%
  tbl_summary(
    missing_text = "Missing"
  ) %>%
  modify_header(
    label = "**Variable**"
  ) %>%
  bold_labels() %>%
  modify_spanning_header(stat_0 ~ "**Overall**")

table2 <- tbl_merge(
  tbls = list(table2, table2_overall),
  tab_spanner = FALSE
)

table2
```

```{r eval=FALSE}
table2 %>%
  as_flex_table() %>%
  flextable::save_as_docx(path = "./02_output/reports/table2.docx")
```

## Table for Exposure Time

```{r}
 table3 <- ffi_total %>%
   select(
     gender,
     age_cat,
     education_level,
     economic_activities,
     recent_exposure,
     historical_exposure
   ) %>%
   drop_na(recent_exposure, historical_exposure) %>%
   pivot_longer(
     cols = recent_exposure:historical_exposure,
     names_to = "exposure",
     values_to = "result_exposure"
   ) %>%
   mutate(
    exposure = case_when(
      exposure == "recent_exposure" ~ "Recent Exposure",
      exposure == "historical_exposure" ~ "Historical Exposure"
    )
   ) %>%
   tbl_strata(
    strata = exposure,
    .tbl_fun =
      ~ .x %>%
          tbl_summary(
            by = result_exposure,
            missing_text = "Missing"
          ) %>%
          add_p(
            test = list(all_categorical() ~ "fisher.test.simulate.p.values")
          ) %>%
          bold_p() %>%
          modify_header(label = "**Variable**") %>%
          bold_labels() 
   ) 

table3_overall <- ffi_total %>%
  select(
    gender,
    age_cat,
    education_level,
    economic_activities,
    recent_exposure,
    historical_exposure
  ) %>%
  drop_na(recent_exposure, historical_exposure) %>%
  tbl_summary(
    missing_text = "Missing"
  ) %>%
  modify_header(
    label = "**Variable**"
  ) %>%
  bold_labels() %>%
  modify_spanning_header(stat_0 ~ "**Overall**")

table3 <- tbl_merge(
  tbls = list(table3, table3_overall),
  tab_spanner = FALSE
)

table3
```

```{r eval=FALSE}
table3 %>%
  as_flex_table() %>%
  flextable::save_as_docx(path = "./02_output/reports/table3.docx")
```


# Distance communities and regional Hospital

```{r}
iquitos_centro <- tibble(
  "ffi_h_health_facility_name" = "Hospital Regional de Loreto",
  Longitude = -73.25385902080906,
  Latitude = -3.7264060164148716,
) %>%
  st_as_sf(coords = c("Longitude", "Latitude"), crs = 4326) %>%
  st_transform(crs = 32718)

communities_sf <- ffi_household %>%
  select(
    ffi_h_district,
    ffi_h_code_community,
    ffi_h_community,
    ffi_gps_long,
    ffi_gps_lat
  ) %>%
  st_as_sf(
    coords = c("ffi_gps_long", "ffi_gps_lat"),
    crs = 4326
  ) %>%
  group_by(ffi_h_district, ffi_h_code_community, ffi_h_community) %>%
  summarise() %>%
  st_centroid()
  

communities_distances <- communities_sf %>%
  st_transform(crs = 32718) %>%
  st_distance(iquitos_centro)

communities_distances <- enframe(communities_distances) %>%
  mutate(
    ffi_is_cod_com = communities_sf$ffi_h_code_community,
    ffi_is_community = communities_sf$ffi_h_community,
    distance = as.numeric(value)
  ) %>%
  select(-c(name, value))
```

Las Comunidades mas cercanas y mas lejanas por distrito:

```{r eval=FALSE}
communities_distances %>%
  mutate(
    ffi_h_district = communities_sf$ffi_h_district
  ) %>%
  group_by(ffi_h_district) %>%
  slice_min(distance, n = 1) %>%
  bind_rows(
    communities_distances %>%
      mutate(
        ffi_h_district = communities_sf$ffi_h_district
      ) %>%
      group_by(ffi_h_district) %>%
      slice_max(distance, n = 1)
  ) %>%
  arrange(ffi_h_district, distance)
```

# Plots

## Malaria Annual Parasite Index


```{r}
data_malaria <- bind_rows(
  data_2000_2019,
  data_2020_2021
) %>%
  drop_na(District)

pob_loreto <- poblacion %>%
  filter(dep == "LORETO") %>%
  select(District = distr, Total)
```


```{r}
malaria_cases_pob <- data_malaria %>%
  rowwise() %>%
  mutate(
    cases_malaria = sum(
      c_across(c(
        `Confirmed P. Falciparum`,
        `Confirmed P. Vivax`
      )),
      na.rm = TRUE
    )
  ) %>%
  group_by(District) %>%
  summarise(cases_malaria = sum(cases_malaria))

malaria_cases_pob <- malaria_cases_pob %>%
  left_join(
    pob_loreto
  ) %>%
  mutate(
    api = cases_malaria * 1000 / Total
  )

data(Peru, package = "innovar")

malaria_cases_pob_sf <- Peru %>%
  filter(dep == "LORETO") %>%
  select(District = distr, geometry) %>% 
  right_join(
    malaria_cases_pob
  )
```


```{r}
figure1 <- ggplot(malaria_cases_pob_sf) +
  geom_sf(aes(fill = api, geometry = geometry)) +
  geom_sf(data = malaria_cases_pob_sf %>% dplyr::filter(District == "BELEN"), colour = "#aa6439", fill = NA, size = 1.5, aes(fill = api, geometry = geometry)) +
  geom_sf(data = malaria_cases_pob_sf %>% dplyr::filter(District == "INDIANA"), colour = "#256e5d", fill = NA, size = 1.5, aes(fill = api, geometry = geometry)) +
  scale_fill_distiller(
    name = "API",
    na.value = "black",
    # limits = c(0, 4000),
    palette = "RdYlGn",
    direction = -1,
    breaks = scales::pretty_breaks(n = 5),
    values = c(
      0,
      0.001,
      0.002,
      0.005,
      0.008,
      0.01,
      0.02,
      0.03,
      0.05,
      0.1,
      0.6,
      0.9,
      1
    )
  ) +
  labs(
    x = NULL,
    y = NULL,
    title = NULL
  ) +
  theme_classic() +
  geom_sf_label_repel(
    data = malaria_cases_pob_sf %>% dplyr::filter(District == "BELEN"),
    aes(label = District),
    min.segment.length = 0,
    force = 100,
    nudge_x = -1,
    seed = 10,
    colour = "#aa6439"
  ) +
  geom_sf_label_repel(
    data = malaria_cases_pob_sf %>% dplyr::filter(District == "INDIANA"),
    aes(label = District),
    min.segment.length = 0,
    force = 100,
    nudge_x = 1,
    seed = 10,
    colour = "#256e5d"
  )

figure1
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/figure1.png",
  figure1,
  device = grDevices::png,
  dpi = 300
)
```


## Serological Results

### By communities and type of plasmodium/time
```{r}
# ffi_total %>%
#   ggplot(aes(
#     x = ffi_is_community,
#     fill = pv_historic
#   )) +
#   coord_flip(ylim = c(0, 0.25)) +
#   geom_bar(position = "fill")

plot1 <- ffi_total %>%
  drop_na(pf_recent:pv_historic) %>%
  left_join(
    communities_distances
  )  %>%
  pivot_longer(
    cols = pf_recent:pv_historic,
    names_to = "malaria",
    values_to = "result_malaria"
  ) %>%
  mutate(
    malaria = case_when(
      malaria == "pf_recent" ~ "Recent P. Falciparum",
      malaria == "pf_historic" ~ "Historical P. Falciparum",
      malaria == "pv_recent" ~ "Recent P. Vivax",
      malaria == "pv_historic" ~ "Historical P. Vivax"
    ),
    ffi_is_community = str_to_title(ffi_is_community),
    ffi_is_community = paste0(
      ffi_is_community,
      " (",
      str_to_title(ffi_is_district),
      ")"
    ),
    ffi_is_community = fct_reorder(
      ffi_is_community,
      distance,
      .desc = TRUE
    )
  ) %>%
  ggplot(aes(
    x = ffi_is_community,
    fill = result_malaria
  )) +
  facet_wrap(vars(malaria)) +
  geom_bar(position = "fill", color = "black") +
  coord_flip(ylim = c(0, 0.25)) +
  scale_y_continuous(
    labels = scales::percent_format()
  ) +
  #ggsci::scale_fill_lancet() +
  innovar::scale_fill_innova("npr") +
  labs(
    x = "Communities",
    y = "Percentage"
  ) +
  guides(
    fill = guide_legend("Results")
  ) +  
  theme_minimal() +
  theme(
    strip.text = element_text(
      face = "bold",
      size = 11
    ),
    axis.title = element_text(
      face = "bold",
      size = 11
    ),
    legend.title = element_text(
      face = "bold",
      size = 11
    )
  )

plot1
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/plot1_typeofmalaria_communities.png",
  plot1,
  dpi = 300,
  bg = "white",
  width = 10,
  height = 9
)
```


### By communities, Indiana and type of plasmodium/time
```{r}
plot1_indiana <- ffi_total %>%
  drop_na(pf_recent:pv_historic) %>%
  left_join(
    communities_distances
  )  %>%
  pivot_longer(
    cols = pf_recent:pv_historic,
    names_to = "malaria",
    values_to = "result_malaria"
  ) %>%
  mutate(
    malaria = case_when(
      malaria == "pf_recent" ~ "Recent P. Falciparum",
      malaria == "pf_historic" ~ "Historical P. Falciparum",
      malaria == "pv_recent" ~ "Recent P. Vivax",
      malaria == "pv_historic" ~ "Historical P. Vivax"
    ),
    ffi_is_community = str_to_title(ffi_is_community)
  ) %>%
  filter(ffi_is_district == "INDIANA") %>%
  mutate(
    ffi_is_community = fct_reorder(
      ffi_is_community,
      distance,
      .desc = TRUE
    )
  ) %>%
  ggplot(aes(
    x = ffi_is_community,
    fill = result_malaria
  )) +
  facet_wrap(vars(malaria)) +
  geom_bar(position = "fill", color = "black") +
  coord_flip(ylim = c(0, 0.25)) +
  scale_y_continuous(
    labels = scales::percent_format()
  ) +
  #ggsci::scale_fill_lancet() +
  innovar::scale_fill_innova("npr") +
  labs(
    title = str_wrap("Serological results by plasmodium type of malaria in the Indiana District", 100),
    x = "Communities",
    y = "Percentage"
  ) +
  guides(
    fill = guide_legend("Results")
  ) +  
  theme_minimal() +
  theme(
    strip.text = element_text(
      face = "bold",
      size = 11
    ),
    axis.title = element_text(
      face = "bold",
      size = 11
    ),
    legend.title = element_text(
      face = "bold",
      size = 11
    )
  )

plot1_indiana
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/plot1_typeofmalaria_communities_indiana.png",
  plot1_indiana,
  dpi = 300,
  bg = "white",
  width = 10,
  height = 9
)
```


### By communities, Belen and type of plasmodium/time
```{r}
plot1_belen <- ffi_total %>%
  drop_na(pf_recent:pv_historic) %>%
  left_join(
    communities_distances
  ) %>%
  pivot_longer(
    cols = pf_recent:pv_historic,
    names_to = "malaria",
    values_to = "result_malaria"
  ) %>%
  filter(ffi_is_district == "BELEN") %>%
  mutate(
    malaria = case_when(
      malaria == "pf_recent" ~ "Recent P. Falciparum",
      malaria == "pf_historic" ~ "Historical P. Falciparum",
      malaria == "pv_recent" ~ "Recent P. Vivax",
      malaria == "pv_historic" ~ "Historical P. Vivax"
    ),
    ffi_is_community = str_to_title(ffi_is_community),
    ffi_is_community = fct_reorder(
      ffi_is_community,
      distance,
      .desc = TRUE
    )
  ) %>%
  ggplot(aes(
    x = ffi_is_community,
    fill = result_malaria
  )) +
  facet_wrap(vars(malaria)) +
  geom_bar(position = "fill", color = "black") +
  coord_flip(ylim = c(0, 0.25)) +
  scale_y_continuous(
    labels = scales::percent_format()
  ) +
  #ggsci::scale_fill_lancet() +
  innovar::scale_fill_innova("npr") +
  labs(
    title = str_wrap("Serological results by plasmodium type of malaria in the Belen District", 100),
    x = "Communities",
    y = "Percentage"
  ) +
  guides(
    fill = guide_legend("Results")
  ) +  
  theme_minimal() +
  theme(
    strip.text = element_text(
      face = "bold",
      size = 11
    ),
    axis.title = element_text(
      face = "bold",
      size = 11
    ),
    legend.title = element_text(
      face = "bold",
      size = 11
    )
  )

plot1_belen
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/plot1_typeofmalaria_communities_belen.png",
  plot1_belen,
  dpi = 300,
  bg = "white",
  width = 10,
  height = 9
)
```

### By communities, type of plasmodium exposure
```{r}
plot2 <- ffi_total %>%
  drop_na(pf_exposure, pv_exposure) %>%
  left_join(
    communities_distances
  ) %>%
  pivot_longer(
    cols = pv_exposure:pf_exposure,
    names_to = "exposure",
    values_to = "result_exposure"
  )  %>%
  mutate(
    exposure = case_when(
         exposure == "pv_exposure" ~ "P. Vivax Exposure",
         exposure == "pf_exposure" ~ "P. Falciparum Exposure"
    ),
    ffi_is_community = str_to_title(ffi_is_community),
    ffi_is_community = fct_reorder(
      ffi_is_community,
      distance,
      .desc = TRUE
    )
  ) %>%
  ggplot(aes(
    x = ffi_is_community,
    fill = result_exposure
  )) +
  facet_wrap(vars(exposure)) +
  geom_bar(position = "fill", color = "black") +
  coord_flip(ylim = c(0, 0.40)) +
  scale_y_continuous(
    labels = scales::percent_format()
  ) +
  # ggsci::scale_fill_lancet() +
  innovar::scale_fill_innova("npr") +
  labs(
    x = "Communities",
    y = "Percentage"
  ) +
  guides(
    fill = guide_legend("Results")
  ) +
  theme_minimal() +
  theme(
    strip.text = element_text(
      face = "bold",
      size = 11
    ),
    axis.title = element_text(
      face = "bold",
      size = 11
    ),
    legend.title = element_text(
      face = "bold",
      size = 11
    )
  )

plot2
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/plot2_typeofmalaria_communities.png",
  plot2,
  dpi = 300,
  bg = "white",
  width = 10,
  height = 9
)
```

### By communities, time of plasmodium exposure
```{r}
plot3 <- ffi_total %>%
  drop_na(recent_exposure, historical_exposure) %>%
  left_join(
    communities_distances
  ) %>%
  pivot_longer(
    cols = recent_exposure:historical_exposure,
    names_to = "exposure",
    values_to = "result_exposure"
  ) %>%
  mutate(
    exposure = case_when(
      exposure == "recent_exposure" ~ "Recent Exposure",
      exposure == "historical_exposure" ~ "Historical Exposure"
    ),
    ffi_is_community = str_to_title(ffi_is_community),
    ffi_is_community = fct_reorder(
      ffi_is_community,
      distance,
      .desc = TRUE
    )
  ) %>%
  ggplot(aes(
    x = ffi_is_community,
    fill = result_exposure
  )) +
  facet_wrap(vars(exposure)) +
  geom_bar(position = "fill", color = "black") +
  coord_flip(ylim = c(0, 0.30)) +
  scale_y_continuous(
    labels = scales::percent_format()
  ) +
  # ggsci::scale_fill_lancet() +
  innovar::scale_fill_innova("npr") +
  labs(
    x = "Communities",
    y = "Percentage"
  ) +
  guides(
    fill = guide_legend("Results")
  ) +
  theme_minimal() +
  theme(
    strip.text = element_text(
      face = "bold",
      size = 11
    ),
    axis.title = element_text(
      face = "bold",
      size = 11
    ),
    legend.title = element_text(
      face = "bold",
      size = 11
    )
  )

plot3
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/plot3_typeofmalaria_communities.png",
  plot3,
  dpi = 300,
  bg = "white",
  width = 10,
  height = 9
)
```


```{r}
# ffi_total %>%
#   drop_na(pf_recent:pv_historic, age_cat) %>%
#   pivot_longer(
#     cols = pf_recent:pv_historic,
#     names_to = "malaria",
#     values_to = "result_malaria"
#   ) %>%
#   mutate(
#     result_malaria = case_when(
#       result_malaria == "Positive" ~ 1,
#       TRUE ~ 0
#     ),
#     malaria = case_when(
#       malaria == "pf_recent" ~ "Recent P. Falciparum",
#       malaria == "pf_historic" ~ "Historical P. Falciparum",
#       malaria == "pv_recent" ~ "Recent P. Vivax",
#       malaria == "pv_historic" ~ "Historical P. Vivax"
#     ),
#     ffi_is_community = str_to_title(ffi_is_community)
#   ) %>%
#   group_by(ffi_is_community, age_cat, malaria) %>%
#   summarise(result_malaria = mean(result_malaria)) %>%
#   ggplot(
#     aes(
#       x = age_cat,
#       y = result_malaria,
#       color = malaria,
#       group = malaria
#     )
#   ) +
#   geom_point() +
#   geom_line() +
#   facet_wrap(vars(ffi_is_community)) +
#   theme_bw()
```

## Seropositivity 4 malaria

### By district
```{r}
sero_4malaria_district <- ffi_total %>%
  drop_na(pf_recent:pv_historic, age_cat) %>%
  pivot_longer(
    cols = pf_recent:pv_historic,
    names_to = "malaria",
    values_to = "result_malaria"
  ) %>%
  mutate(
    result_malaria = case_when(
      result_malaria == "Positive" ~ 1,
      TRUE ~ 0
    ),
    malaria = case_when(
      malaria == "pf_recent" ~ "Recent P. Falciparum",
      malaria == "pf_historic" ~ "Historical P. Falciparum",
      malaria == "pv_recent" ~ "Recent P. Vivax",
      malaria == "pv_historic" ~ "Historical P. Vivax"
    ),
    across(
      c(ffi_is_community:ffi_is_health_facility_name),
      str_to_title
    )
  ) %>%
  group_by(ffi_is_district, age_cat, malaria) %>%
  summarise(result_malaria = mean(result_malaria)) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_malaria,
      color = malaria,
      group = malaria
    )
  ) +
  geom_point() +
  geom_line() +
  facet_wrap(vars(ffi_is_district)) +
  scale_y_continuous(
    labels = scales::percent_format(),
    limits = c(0, 0.40)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

sero_4malaria_district
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/plot4_sero_4malaria_district.png",
  sero_4malaria_district,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 7.5
)
```


### By Health Facility

```{r}
sero_4malaria_hf <- ffi_total %>%
  drop_na(pf_recent:pv_historic, age_cat) %>%
  pivot_longer(
    cols = pf_recent:pv_historic,
    names_to = "malaria",
    values_to = "result_malaria"
  ) %>%
  mutate(
    result_malaria = case_when(
      result_malaria == "Positive" ~ 1,
      TRUE ~ 0
    ),
    malaria = case_when(
      malaria == "pf_recent" ~ "Recent P. Falciparum",
      malaria == "pf_historic" ~ "Historical P. Falciparum",
      malaria == "pv_recent" ~ "Recent P. Vivax",
      malaria == "pv_historic" ~ "Historical P. Vivax"
    ),
    across(
      c(ffi_is_community:ffi_is_health_facility_name),
      str_to_title
    ),
    ffi_is_health_facility_name = paste(
      ffi_is_district,
      ffi_is_health_facility_name,
      sep = " - "
    )
  ) %>%
  group_by(ffi_is_health_facility_name, age_cat, malaria) %>%
  summarise(result_malaria = mean(result_malaria)) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_malaria,
      color = malaria,
      group = malaria
    )
  ) +
  geom_point() +
  geom_line() +
  facet_wrap(vars(ffi_is_health_facility_name)) +
  scale_y_continuous(
    labels = scales::percent_format()
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") + 
  theme_bw() +
  theme(
    axis.text.x = element_text(
      angle = 45,
      vjust = 1,
      hjust = 1
    )
  )
  
sero_4malaria_hf
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/plot5_sero_4malaria_hf.png",
  sero_4malaria_hf,
  dpi = 300,
  bg = "white",
  width = 10,
  height = 7
)
```

## Seopositivity by Type of Malaria

### By District

```{r}
ffi_typeof_malaria <- ffi_total %>%
  drop_na(pf_exposure, pv_exposure, age_cat) %>%
  pivot_longer(
    cols = pv_exposure:pf_exposure,
    names_to = "exposure",
    values_to = "result_exposure"
  ) %>%
  mutate(
    exposure = case_when(
      exposure == "pv_exposure" ~ "P. Vivax Exposure",
      exposure == "pf_exposure" ~ "P. Falciparum Exposure"
    ),
    result_exposure = case_when(
      result_exposure == "Positive" ~ 1,
      TRUE ~ 0
    ),
    across(
      c(ffi_is_community:ffi_is_health_facility_name),
      str_to_title
    )
  )

sero_typeofmalaria_district <- ffi_typeof_malaria %>%
  group_by(ffi_is_district, age_cat, exposure) %>%
  summarise(result_exposure = mean(result_exposure)) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_exposure,
      color = exposure,
      group = exposure
    )
  ) +
  geom_point() +
  geom_line() +
  facet_wrap(vars(ffi_is_district)) +
  scale_y_continuous(
    labels = scales::percent_format(),
    limits = c(0, 0.40)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/plot6_sero_typeofmalaria_district.png",
  sero_typeofmalaria_district,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 7.5
)
```

### By Health Facility

```{r}
sero_typeofmalaria_hf <- ffi_typeof_malaria %>%
  group_by(ffi_is_health_facility_name, age_cat, exposure) %>%
  summarise(result_exposure = mean(result_exposure)) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_exposure,
      color = exposure,
      group = exposure
    )
  ) +
  geom_point() +
  geom_line() +
  facet_wrap(vars(ffi_is_health_facility_name)) +
  scale_y_continuous(
    labels = scales::percent_format()
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

sero_typeofmalaria_hf
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/plot7_sero_typeofmalaria_hf.png",
  sero_typeofmalaria_hf,
  dpi = 300,
  bg = "white",
  width = 13,
  height = 9
)
```

## Seropositivity by Time of Exposure 

### By District
```{r}
ffi_timeofmalaria <- ffi_total %>%
  drop_na(recent_exposure, historical_exposure, age_cat) %>%
  pivot_longer(
    cols = recent_exposure:historical_exposure,
    names_to = "exposure",
    values_to = "result_exposure"
  ) %>%
  mutate(
    exposure = case_when(
      exposure == "recent_exposure" ~ "Recent Exposure",
      exposure == "historical_exposure" ~ "Historical Exposure"
    ),
    result_exposure = case_when(
      result_exposure == "Positive" ~ 1,
      TRUE ~ 0
    ),
    across(
      c(ffi_is_community:ffi_is_health_facility_name),
      str_to_title
    )
  )

sero_timeofmalaria_district <- ffi_timeofmalaria %>%
  group_by(ffi_is_district, age_cat, exposure) %>%
  summarise(result_exposure = mean(result_exposure)) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_exposure,
      color = exposure,
      group = exposure
    )
  ) +
  geom_point() +
  geom_line() +
  facet_wrap(vars(ffi_is_district)) +
  scale_y_continuous(
    labels = scales::percent_format(),
    limits = c(0, 0.40)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/plot8_sero_timeofmalaria_district.png",
  sero_timeofmalaria_district,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 7.5
)
```

### By Health Facility

```{r}
sero_timeofmalaria_hf <- ffi_timeofmalaria %>%
  group_by(ffi_is_health_facility_name, age_cat, exposure) %>%
  summarise(result_exposure = mean(result_exposure)) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_exposure,
      color = exposure,
      group = exposure
    )
  ) +
  geom_point() +
  geom_line() +
  facet_wrap(vars(ffi_is_health_facility_name)) +
  scale_y_continuous(
    labels = scales::percent_format()
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") + 
  theme_bw()
  
sero_timeofmalaria_hf
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/plot9_sero_timeofmalaria_hf.png",
  sero_timeofmalaria_hf,
  dpi = 300,
  bg = "white",
  width = 13,
  height = 9
)
```

## Relation with sex

```{r}
library(rlang)

seropositivy_summarise <- function(data, type, variable, ...) {
  if (type == "4malaria") {
    ffi_total %>%
      drop_na(pf_recent:pv_historic, {{ variable }}, ...) %>%
      pivot_longer(
        cols = pf_recent:pv_historic,
        names_to = "malaria",
        values_to = "result_malaria"
      ) %>%
      mutate(
        result_malaria = case_when(
          result_malaria == "Positive" ~ 1,
          TRUE ~ 0
        ),
        malaria = case_when(
          malaria == "pf_recent" ~ "Recent P. Falciparum",
          malaria == "pf_historic" ~ "Historical P. Falciparum",
          malaria == "pv_recent" ~ "Recent P. Vivax",
          malaria == "pv_historic" ~ "Historical P. Vivax"
        ),
        across(
          c(ffi_is_community:ffi_is_health_facility_name),
          str_to_title
        )
      ) %>%
      group_by(ffi_is_district, {{ variable }}, ..., malaria) %>%
      summarise(result_malaria = mean(result_malaria))
  } else if (type == "typeofmalaria" ) {
    ffi_total %>%
      drop_na(pf_exposure, pv_exposure, {{ variable }}, ...) %>%
      pivot_longer(
        cols = pv_exposure:pf_exposure,
        names_to = "exposure",
        values_to = "result_exposure"
      ) %>%
      mutate(
        exposure = case_when(
          exposure == "pv_exposure" ~ "P. Vivax Exposure",
          exposure == "pf_exposure" ~ "P. Falciparum Exposure"
        ),
        result_exposure = case_when(
          result_exposure == "Positive" ~ 1,
          TRUE ~ 0
        ),
        across(
          c(ffi_is_community:ffi_is_health_facility_name),
          str_to_title
        )
      ) %>%
      group_by(ffi_is_district, {{ variable }}, ..., exposure) %>%
      summarise(result_exposure = mean(result_exposure))
  } else if (type == "timeofmalaria") {
    ffi_total %>%
      drop_na(recent_exposure, historical_exposure, 
              {{ variable }}, ...) %>%
      pivot_longer(
        cols = recent_exposure:historical_exposure,
        names_to = "exposure",
        values_to = "result_exposure"
      ) %>%
      mutate(
        exposure = case_when(
          exposure == "recent_exposure" ~ "Recent Exposure",
          exposure == "historical_exposure" ~ "Historical Exposure"
        ),
        result_exposure = case_when(
          result_exposure == "Positive" ~ 1,
          TRUE ~ 0
        ),
        across(
          c(ffi_is_community:ffi_is_health_facility_name),
          str_to_title
        )
      ) %>%
      group_by(ffi_is_district, {{ variable }}, ..., exposure) %>%
      summarise(result_exposure = mean(result_exposure))      
  }  
}
```

### By 4 Malaria

```{r}
sero_4malaria_district_gender <- seropositivy_summarise(
  ffi_total,
  "4malaria",
  age_cat,
  gender
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_malaria,
      color = malaria,
      group = malaria
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(gender),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    limits = c(0, 0.40)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

sero_4malaria_district_gender
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/plot10_sero_4malaria_district_gender.png",
  sero_4malaria_district_gender,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)
```

### By Type of Malaria

```{r}
sero_typeofmalaria_district_gender <- seropositivy_summarise(
  ffi_total,
  "typeofmalaria",
  age_cat,
  gender
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_exposure,
      color = exposure,
      group = exposure
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(gender),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    limits = c(0, 0.45)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

sero_typeofmalaria_district_gender
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/plot11_sero_typeofmalaria_district_gender.png",
  sero_typeofmalaria_district_gender,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)
```

### By Time of Malaria

```{r}
sero_timeofmalaria_district_gender <- seropositivy_summarise(
  ffi_total,
  "timeofmalaria",
  age_cat,
  gender
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_exposure,
      color = exposure,
      group = exposure
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(gender),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    limits = c(0, 0.45)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

sero_timeofmalaria_district_gender
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/plot12_sero_timeofmalaria_district_gender.png",
  sero_timeofmalaria_district_gender,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)
```

## Relation with Education Level

### By 4 Malaria

```{r}
sero_4malaria_district_edulevel <- seropositivy_summarise(
  ffi_total,
  "4malaria",
  age_cat,
  education_level
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_malaria,
      color = malaria,
      group = malaria
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(education_level),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    #limits = c(0, 0.40)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

sero_4malaria_district_edulevel
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/plot13_sero_4malaria_district_edulevel.png",
  sero_4malaria_district_edulevel,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)
```

### By Type of Malaria

```{r}
sero_typeofmalaria_district_edulevel <- seropositivy_summarise(
  ffi_total,
  "typeofmalaria",
  age_cat,
  education_level
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_exposure,
      color = exposure,
      group = exposure
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(education_level),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    #limits = c(0, 0.45)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

sero_typeofmalaria_district_edulevel
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/plot14_sero_typeofmalaria_district_edulevel.png",
  sero_typeofmalaria_district_edulevel,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)
```

### By Time of Malaria

```{r}
sero_timeofmalaria_district_edulevel <- seropositivy_summarise(
  ffi_total,
  "timeofmalaria",
  age_cat,
  education_level
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_exposure,
      color = exposure,
      group = exposure
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(education_level),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    #limits = c(0, 0.45)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

sero_timeofmalaria_district_edulevel
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/plot15_sero_timeofmalaria_district_edulevel.png",
  sero_timeofmalaria_district_edulevel,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)
```

## Relation with Fever Month

#### By 4 malaria
```{r}
ffi_fever_month <- ffi_total %>%
  drop_na(pf_recent:pv_historic, age_cat, ffi_is_fever_month) %>%
  pivot_longer(
    cols = pf_recent:pv_historic,
    names_to = "malaria",
    values_to = "result_malaria"
  ) %>%
  mutate(
    result_malaria = case_when(
      result_malaria == "Positive" ~ 1,
      TRUE ~ 0
    ),
    malaria = case_when(
      malaria == "pf_recent" ~ "Recent P. Falciparum",
      malaria == "pf_historic" ~ "Historical P. Falciparum",
      malaria == "pv_recent" ~ "Recent P. Vivax",
      malaria == "pv_historic" ~ "Historical P. Vivax"
    ),
    across(
      c(ffi_is_community:ffi_is_health_facility_name),
      str_to_title
    )
  ) %>%
  group_by(ffi_is_fever_month, age_cat, malaria) %>%
  summarise(result_malaria = mean(result_malaria))


sero_4malaria_fever_month <- ffi_fever_month %>%
  ggplot(
    aes(
      x = result_malaria,
      y = age_cat,
      group = age_cat
    )
  ) +
  geom_path(
    color = "#bdbdbd"
  ) +
  geom_point(
    aes(color = malaria),
    size = 3
  ) +
  facet_wrap(vars(ffi_is_fever_month)) +
  scale_x_continuous(
    labels = scales::percent_format(),
    #limits = c(0, 0.5)
  ) +
  labs(
    y = "Age",
    x = "Seropositivity",
    title = str_wrap("Malaria seropositivity by type of exposure, plasmodium and presence of fever in the last month", 100)
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/plot16_sero_4malaria_fevermonth.png",
  sero_4malaria_fever_month,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)
```


#### By Type of Malaria

```{r}
ffi_fever_month_typeofmalaria <- ffi_total %>%
  drop_na(pf_recent:pv_historic, age_cat, ffi_is_fever_month) %>%
  pivot_longer(
    cols = pv_exposure:pf_exposure,
    names_to = "exposure",
    values_to = "result_exposure"
  ) %>%
    mutate(
      exposure = case_when(
        exposure == "pv_exposure" ~ "P. Vivax Exposure",
        exposure == "pf_exposure" ~ "P. Falciparum Exposure"
      ),
      result_exposure = case_when(
        result_exposure == "Positive" ~ 1,
        TRUE ~ 0
      ),
      across(
        c(ffi_is_community:ffi_is_health_facility_name),
        str_to_title
      )
    ) %>%
    group_by(ffi_is_fever_month, age_cat, exposure) %>%
    summarise(result_exposure = mean(result_exposure))

sero_typeofmalaria_fever_month <- ffi_fever_month_typeofmalaria %>%
  ggplot(
    aes(
      x = result_exposure,
      y = age_cat,
      group = age_cat
    )
  ) +
  geom_path(
    color = "#bdbdbd"
  ) +
  geom_point(
    aes(color = exposure),
    size = 3
  ) +
  facet_wrap(vars(ffi_is_fever_month)) +
  scale_x_continuous(
    labels = scales::percent_format(),
    # limits = c(0, 0.5)
  ) +
  labs(
    y = "Age",
    x = "Seropositivity",
    title = str_wrap("Malaria seropositivity by type of plasmodium and presence of fever in the last month", 100)
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/plot16_sero_typeofmalaria_fevermonth.png",
  sero_typeofmalaria_fever_month,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)
```

#### By Time of Malaria


```{r}
ffi_fever_month_timeofmalaria <- ffi_total %>%
  drop_na(pf_recent:pv_historic, age_cat, ffi_is_fever_month) %>%
  pivot_longer(
    cols = recent_exposure:historical_exposure,
    names_to = "exposure",
    values_to = "result_exposure"
  ) %>%
  mutate(
    exposure = case_when(
      exposure == "recent_exposure" ~ "Recent Exposure",
      exposure == "historical_exposure" ~ "Historical Exposure"
    ),
    result_exposure = case_when(
      result_exposure == "Positive" ~ 1,
      TRUE ~ 0
    ),
    across(
      c(ffi_is_community:ffi_is_health_facility_name),
      str_to_title
    )
  ) %>%
  group_by(ffi_is_fever_month, age_cat, exposure) %>%
  summarise(result_exposure = mean(result_exposure))

sero_timeofmalaria_fever_month <- ffi_fever_month_typeofmalaria %>%
  ggplot(
    aes(
      x = result_exposure,
      y = age_cat,
      group = age_cat
    )
  ) +
  geom_path(
    color = "#bdbdbd"
  ) +
  geom_point(
    aes(color = exposure),
    size = 3
  ) +
  facet_wrap(vars(ffi_is_fever_month)) +
  scale_x_continuous(
    labels = scales::percent_format(),
    # limits = c(0, 0.5)
  ) +
  labs(
    y = "Age",
    x = "Seropositivity",
    title = str_wrap("Malaria seropositivity by time of plasmodium and presence of fever in the last month", 100)
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/plot16_sero_timeofmalaria_fevermonth.png",
  sero_timeofmalaria_fever_month,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)
```


## Relation with Antimalarial Drugs

### By 4 Malaria

```{r}
sero_4malaria_district_antimaldrugs <- seropositivy_summarise(
  ffi_total,
  "4malaria",
  age_cat,
  ffi_is_antimal_drug_use
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_malaria,
      color = malaria,
      group = malaria
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(ffi_is_antimal_drug_use),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    limits = c(0, 0.50)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

sero_4malaria_district_antimaldrugs
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/plot19_sero_4malaria_district_antimaldrugs.png",
  sero_4malaria_district_antimaldrugs,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)
```

### By Type of Malaria

```{r}
sero_typeofmalaria_district_antimaldrugs <- seropositivy_summarise(
  ffi_total,
  "typeofmalaria",
  age_cat,
  ffi_is_antimal_drug_use
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_exposure,
      color = exposure,
      group = exposure
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(ffi_is_antimal_drug_use),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    limits = c(0, 0.60)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

sero_typeofmalaria_district_antimaldrugs
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/plot20_sero_typeofmalaria_district_antimaldrugs.png",
  sero_typeofmalaria_district_antimaldrugs,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)
```

### By Time of Malaria

```{r}
sero_timeofmalaria_district_antimaldrugs <- seropositivy_summarise(
  ffi_total,
  "timeofmalaria",
  age_cat,
  ffi_is_antimal_drug_use
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_exposure,
      color = exposure,
      group = exposure
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(ffi_is_antimal_drug_use),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    limits = c(0, 0.5)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

sero_timeofmalaria_district_antimaldrugs
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/plot21_sero_timeofmalaria_district_antimaldrugs.png",
  sero_timeofmalaria_district_antimaldrugs,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)
```


## Relation with time of someone had malaria

### By general malaria

```{r}
ffi_4malaria_mal_lifetime <- ffi_total %>% 
  drop_na(only_pv_exposure:freedom_malaria, 
          age_code, ffi_is_mal_lifetime) %>% 
  pivot_longer(
    cols = only_pv_exposure:freedom_malaria,
    names_to = "malaria",
    values_to = "result_malaria"
  ) %>% 
  mutate(
    result_malaria = case_when(
      result_malaria == "Positive" ~ 1,
      TRUE ~ 0
    ),
    malaria = case_when(
      malaria == "only_pv_exposure" ~ "Only P. vivax",
      malaria == "only_pf_exposure" ~ "Only P. falciparum",
      malaria == "pv_pf_exposure" ~ "P. vivax & falciparum Exposure",
      malaria == "freedom_malaria" ~ "Freedom from Malaria"
    ),
    across(
      c(ffi_is_community:ffi_is_health_facility_name),
      str_to_title
    )
  ) %>%
  group_by(ffi_is_mal_lifetime, age_code, malaria) %>%
  summarise(result_malaria = mean(result_malaria))

sero_4malaria_mal_lifetime <- ffi_4malaria_mal_lifetime %>%
  ggplot(
    aes(
      x = age_code,
      y = result_malaria,
      fill = malaria
    )
  ) +
  geom_area() +
  scale_x_continuous(
    limits = c(1, 8),
    breaks = seq(1, 8, 1),
    labels = c(
      "[0-10)",
      "[10-20)",
      "[20-30)",
      "[30-40)",
      "[40-50)",
      "[50-60)",
      "[60-70)",
      "[70+)"
    ),
    expand = c(0, 0)
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    expand = c(0, 0)
  ) +
  facet_wrap(vars(ffi_is_mal_lifetime)) +
  labs(
    y = "Seropositivity",
    x = "Age",
    title = str_wrap("Malaria seropositivity by type of exposure, plasmodium and how many times they think they have had malaria", 100)
  ) +
  guides(
    fill = guide_legend("Malaria")
  ) +
  innovar::scale_fill_innova("npr") +
  theme_bw() +
  theme(
    axis.text.x = element_text(
      angle = 45,
      vjust = 1,
      hjust = 1
    ),
    panel.spacing = unit(1, "lines")
  )

sero_4malaria_mal_lifetime
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/plot22_sero_4ofmalaria_mal_lifetime.png",
  sero_4malaria_mal_lifetime,
  dpi = 300,
  bg = "white",
  width = 11,
  height = 5
)
```

### By Recent Malaria

```{r}
ffi_recentmalaria_mal_lifetime <- ffi_total %>% 
  drop_na(only_pv_recent:freedom_malaria_recent, 
          age_code, ffi_is_mal_lifetime) %>% 
  pivot_longer(
    cols = only_pv_recent:freedom_malaria_recent,
    names_to = "malaria",
    values_to = "result_malaria"
  ) %>% 
  mutate(
    result_malaria = case_when(
      result_malaria == "Positive" ~ 1,
      TRUE ~ 0
    ),
    malaria = case_when(
      malaria == "only_pv_recent" ~ "Only P. vivax Recent",
      malaria == "only_pf_recent" ~ "Only P. falciparum Recent",
      malaria == "pv_pf_recent" ~ "P. vivax & falciparum Recent",
      malaria == "freedom_malaria_recent" ~ "Freedom from Malaria Recent"
    ),
    across(
      c(ffi_is_community:ffi_is_health_facility_name),
      str_to_title
    )
  ) %>%
  group_by(ffi_is_mal_lifetime, age_code, malaria) %>%
  summarise(result_malaria = mean(result_malaria))

sero_recentmalaria_mal_lifetime <- ffi_recentmalaria_mal_lifetime %>%
  ggplot(
    aes(
      x = age_code,
      y = result_malaria,
      fill = malaria
    )
  ) +
  geom_area() +
  scale_x_continuous(
    limits = c(1, 8),
    breaks = seq(1, 8, 1),
    labels = c(
      "[0-10)",
      "[10-20)",
      "[20-30)",
      "[30-40)",
      "[40-50)",
      "[50-60)",
      "[60-70)",
      "[70+)"
    ),
    expand = c(0, 0)
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    expand = c(0, 0)
  ) +
  facet_wrap(vars(ffi_is_mal_lifetime)) +
  labs(
    y = "Seropositivity",
    x = "Age",
    title = str_wrap("Malaria seropositivity by recent exposure and how many times they think they have had malaria", 100)
  ) +
  guides(
    fill = guide_legend("Malaria")
  ) +
  innovar::scale_fill_innova("npr") +
  theme_bw() +
  theme(
    axis.text.x = element_text(
      angle = 45,
      vjust = 1,
      hjust = 1
    ),
    panel.spacing = unit(1, "lines")
  )

sero_recentmalaria_mal_lifetime
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/plot23_sero_recent_malaria_mal_lifetime.png",
  sero_recentmalaria_mal_lifetime,
  dpi = 300,
  bg = "white",
  width = 11,
  height = 5
)
```


### By Historic Malaria

```{r}
ffi_historicmalaria_mal_lifetime <- ffi_total %>% 
  drop_na(only_pv_historic:freedom_malaria_historic, 
          age_code, ffi_is_mal_lifetime) %>% 
  pivot_longer(
    cols = only_pv_historic:freedom_malaria_historic,
    names_to = "malaria",
    values_to = "result_malaria"
  ) %>% 
  mutate(
    result_malaria = case_when(
      result_malaria == "Positive" ~ 1,
      TRUE ~ 0
    ),
    malaria = case_when(
      malaria == "only_pv_historic" ~ "Only P. vivax Historic",
      malaria == "only_pf_historic" ~ "Only P. falciparum Historic",
      malaria == "pv_pf_historic" ~ "P. vivax & falciparum Historic",
      malaria == "freedom_malaria_historic" ~ "Freedom from Malaria Historic"
    ),
    across(
      c(ffi_is_community:ffi_is_health_facility_name),
      str_to_title
    )
  ) %>%
  group_by(ffi_is_mal_lifetime, age_code, malaria) %>%
  summarise(result_malaria = mean(result_malaria))

sero_historicmalaria_mal_lifetime <- ffi_historicmalaria_mal_lifetime %>%
  ggplot(
    aes(
      x = age_code,
      y = result_malaria,
      fill = malaria
    )
  ) +
  geom_area() +
  scale_x_continuous(
    limits = c(1, 8),
    breaks = seq(1, 8, 1),
    labels = c(
      "[0-10)",
      "[10-20)",
      "[20-30)",
      "[30-40)",
      "[40-50)",
      "[50-60)",
      "[60-70)",
      "[70+)"
    ),
    expand = c(0, 0)
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    expand = c(0, 0)
  ) +
  facet_wrap(vars(ffi_is_mal_lifetime)) +
  labs(
    y = "Seropositivity",
    x = "Age",
    title = str_wrap("Malaria seropositivity by historic exposure and how many times they think they have had malaria", 100)
  ) +
  guides(
    fill = guide_legend("Malaria")
  ) +
  innovar::scale_fill_innova("npr") +
  theme_bw() +
  theme(
    axis.text.x = element_text(
      angle = 45,
      vjust = 1,
      hjust = 1
    ),
    panel.spacing = unit(1, "lines")
  )

sero_historicmalaria_mal_lifetime
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/plot24_sero_historic_malaria_mal_lifetime.png",
  sero_historicmalaria_mal_lifetime,
  dpi = 300,
  bg = "white",
  width = 11,
  height = 5
)
```


## Relation where someone usually bathe

### By 4 Malaria

```{r}
sero_4malaria_district_ussualybathe<- seropositivy_summarise(
  ffi_total,
  "4malaria",
  age_cat,
  ffi_is_place_shower
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_malaria,
      color = malaria,
      group = malaria
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(ffi_is_place_shower),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    #limits = c(0, 0.50)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

sero_4malaria_district_ussualybathe
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/plot25_sero_4malaria_district_ussualybathe.png",
  sero_4malaria_district_ussualybathe,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)
```

### By Type of Malaria

```{r}
sero_typeofmalaria_district_ussualybathe <- seropositivy_summarise(
  ffi_total,
  "typeofmalaria",
  age_cat,
  ffi_is_place_shower
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_exposure,
      color = exposure,
      group = exposure
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(ffi_is_place_shower),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    #limits = c(0, 0.60)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

sero_typeofmalaria_district_ussualybathe
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/plot26_sero_typeofmalaria_district_ussualybathe.png",
  sero_typeofmalaria_district_ussualybathe,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)
```

### By Time of Malaria

```{r}
sero_timeofmalaria_district_ussualybathe <- seropositivy_summarise(
  ffi_total,
  "timeofmalaria",
  age_cat,
  ffi_is_place_shower
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_exposure,
      color = exposure,
      group = exposure
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(ffi_is_place_shower),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    #limits = c(0, 0.5)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

sero_timeofmalaria_district_ussualybathe
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/plot27_sero_timeofmalaria_district_ussualybathe.png",
  sero_timeofmalaria_district_ussualybathe,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)
```


## Relation with those who have a mosquito net

### By 4 Malaria

```{r}
sero_4malaria_district_mosquitnet <- seropositivy_summarise(
  ffi_total,
  "4malaria",
  age_cat,
  ffi_is_mosq_net
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_malaria,
      color = malaria,
      group = malaria
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(ffi_is_mosq_net),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    #limits = c(0, 0.50)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

sero_4malaria_district_mosquitnet
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/plot28_sero_4malaria_district_mosquitnet.png",
  sero_4malaria_district_mosquitnet,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)
```

### By Type of Malaria

```{r}
sero_typeofmalaria_district_mosquitnet <- seropositivy_summarise(
  ffi_total,
  "typeofmalaria",
  age_cat,
  ffi_is_mosq_net
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_exposure,
      color = exposure,
      group = exposure
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(ffi_is_mosq_net),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    #limits = c(0, 0.60)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

sero_typeofmalaria_district_mosquitnet
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/plot29_sero_typeofmalaria_district_mosquitnet.png",
  sero_typeofmalaria_district_mosquitnet,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)
```

### By Time of Malaria

```{r}
sero_timeofmalaria_district_mosquitnet <- seropositivy_summarise(
  ffi_total,
  "timeofmalaria",
  age_cat,
  ffi_is_mosq_net
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_exposure,
      color = exposure,
      group = exposure
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(ffi_is_mosq_net),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    #limits = c(0, 0.5)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

sero_timeofmalaria_district_mosquitnet
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/plot30_sero_timeofmalaria_district_mosquitnet.png",
  sero_timeofmalaria_district_mosquitnet,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)
```

## Relation with those who have a trip in the last month

### By 4 Malaria

```{r}
sero_4malaria_district_tripmonth <- seropositivy_summarise(
  ffi_total,
  "4malaria",
  age_cat,
  ffi_is_trip_month
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_malaria,
      color = malaria,
      group = malaria
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(ffi_is_trip_month),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    #limits = c(0, 0.50)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

sero_4malaria_district_tripmonth
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/plot31_sero_4malaria_district_tripmonth.png",
  sero_4malaria_district_tripmonth,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)
```

### By Type of Malaria

```{r}
sero_typeofmalaria_district_tripmonth <- seropositivy_summarise(
  ffi_total,
  "typeofmalaria",
  age_cat,
  ffi_is_trip_month
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_exposure,
      color = exposure,
      group = exposure
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(ffi_is_trip_month),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    #limits = c(0, 0.60)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

sero_typeofmalaria_district_tripmonth
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/plot32_sero_typeofmalaria_district_tripmonth.png",
  sero_typeofmalaria_district_tripmonth,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)
```

### By Time of Malaria

```{r}
sero_timeofmalaria_district_tripmonth <- seropositivy_summarise(
  ffi_total,
  "timeofmalaria",
  age_cat,
  ffi_is_trip_month
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_exposure,
      color = exposure,
      group = exposure
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(ffi_is_trip_month),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    #limits = c(0, 0.5)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

sero_timeofmalaria_district_tripmonth
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/plot33_sero_timeofmalaria_district_tripmonth.png",
  sero_timeofmalaria_district_tripmonth,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)
```

# Descriptive 01_data

```{r eval=FALSE}
plot_4_2 <- ffi_total %>%
  filter(ffi_is_malaria %in% c("No", "Yes")) %>%
  drop_na(age_cat, pv_historic) %>%
  mutate(
    malaria_gender = paste(gender, ffi_is_malaria)
  ) %>%
  count(pv_historic, age_cat, malaria_gender) %>%
  mutate(Percentage = n / sum(n)) %>%
  mutate(
    Percentage = case_when(
      str_detect(malaria_gender, "Female") ~ Percentage * -1,
      TRUE ~ Percentage
    )
  ) %>%
  ggplot(aes(
    y = age_cat,
    x = Percentage,
    fill = malaria_gender
  )) +
  geom_col(
    position = position_stack(reverse = TRUE),
    color = "black"
  ) +
  labs(
    x = "Age Group",
    y = NULL,
    title = "Population structure by age groups, gender and malaria transmission"
  ) +
  facet_wrap(vars(pv_historic)) + 
  scale_x_continuous(
    limits = c(-0.25, 0.25),
    breaks = seq(-0.25, 0.25, 0.05),
    labels = c(paste0(seq(25, 0, -5), "%"), paste0(seq(5, 25, 5), "%"))
  ) +
  # innovar::scale_fill_innova("npr") +
  scale_fill_discrete(
    type = innovar::innova_pal("npr")(4),
    limits = c("Male Yes", "Male No", "Female No", "Female Yes")
  ) +
  guides(
    fill = guide_legend("Have you ever \nhad malaria?")
  ) +
  geom_vline(xintercept = 0, size = 1) +
  theme_bw(base_size = 12) +
  theme(
    plot.title = element_text(
      size = 14,
      hjust = 0.5,
      face = "bold"
    ),
    strip.text = element_text(
      size = 12,
      face = "bold"
    ),
    legend.title = element_text(
      size = 12,
      face = "bold"
    ),
    plot.margin = margin(5, 15, 5, 15)
  )
```

# Descriptive 01_data

```{r eval=FALSE}
plot_4_2 <- ffi_total %>%
  filter(ffi_is_malaria %in% c("No", "Yes")) %>%
  drop_na(age_cat, pv_historic) %>%
  mutate(
    malaria_gender = paste(gender, ffi_is_malaria)
  ) %>%
  count(pv_historic, age_cat, malaria_gender) %>%
  mutate(Percentage = n / sum(n)) %>%
  mutate(
    Percentage = case_when(
      str_detect(malaria_gender, "Female") ~ Percentage * -1,
      TRUE ~ Percentage
    )
  ) %>%
  ggplot(aes(
    y = age_cat,
    x = Percentage,
    fill = malaria_gender
  )) +
  geom_col(
    position = position_stack(reverse = TRUE),
    color = "black"
  ) +
  labs(
    x = "Age Group",
    y = NULL,
    title = "Population structure by age groups, gender and malaria transmission"
  ) +
  facet_wrap(vars(pv_historic)) + 
  scale_x_continuous(
    limits = c(-0.25, 0.25),
    breaks = seq(-0.25, 0.25, 0.05),
    labels = c(paste0(seq(25, 0, -5), "%"), paste0(seq(5, 25, 5), "%"))
  ) +
  # innovar::scale_fill_innova("npr") +
  scale_fill_discrete(
    type = innovar::innova_pal("npr")(4),
    limits = c("Male Yes", "Male No", "Female No", "Female Yes")
  ) +
  guides(
    fill = guide_legend("Have you ever \nhad malaria?")
  ) +
  geom_vline(xintercept = 0, size = 1) +
  theme_bw(base_size = 12) +
  theme(
    plot.title = element_text(
      size = 14,
      hjust = 0.5,
      face = "bold"
    ),
    strip.text = element_text(
      size = 12,
      face = "bold"
    ),
    legend.title = element_text(
      size = 12,
      face = "bold"
    ),
    plot.margin = margin(5, 15, 5, 15)
  )
```

## Sankey_seropositive


```{r}
library(ggsankey)

ffi_format_sankey <- ffi_total %>%
  mutate(
    ffi_is_access_malaria_yn_hf = factor(
      ffi_is_access_malaria_yn_hf,
      label = c("No", "Yes")
    ),
    ffi_is_mal_lifetime = as.character(ffi_is_mal_lifetime),
    ffi_is_mal_lifetime = replace_na(ffi_is_mal_lifetime, "0 times"),
    ffi_is_mal_lifetime = factor(
      ffi_is_mal_lifetime,
      labels = c(
        "0 times",
        "1 to 3 times",
        "3 to 7 times",
        "More than 7 times"
      )
    )
  )

sankey_seropositive_answ1 <- ffi_format_sankey %>%
  pivot_longer(
    cols = c(recent_exposure, ffi_is_mal_lifetime, ffi_is_access_malaria_yn_hf),
    names_to = "malaria_behavior1",
    values_to = "malaria_answ1"
  ) %>%
  count(malaria_behavior1, malaria_answ1) %>%
  group_by(malaria_behavior1) %>%
  mutate(
    percentage = scales::percent(
      n / sum(n),
      accuracy = 0.1
    )
  ) %>%
  ungroup() %>%
  mutate(
    malaria_behavior1 = fct_relevel(
      malaria_behavior1,
      "ffi_is_mal_lifetime",
      "ffi_is_access_malaria_yn_hf",
      "recent_exposure"
    ),
    malaria_percent1 = paste0(
      malaria_answ1,
      paste0("\n(", percentage, ")")
    ),
    malaria_percent1 = as_factor(malaria_percent1)
  ) %>%
  select(malaria_behavior1, malaria_answ1, malaria_percent1)


sankey_seropositive <- ffi_format_sankey %>%
  drop_na(ffi_is_mal_lifetime, 
  ffi_is_access_malaria_yn_hf,
  recent_exposure
  ) %>%
  make_long(
    ffi_is_mal_lifetime,
    ffi_is_access_malaria_yn_hf, 
    recent_exposure
  ) %>%
  left_join(
    sankey_seropositive_answ1,
      by = c(
        "x" = "malaria_behavior1",
        "node" = "malaria_answ1"
      )
  ) %>%
  mutate(
    node = malaria_percent1,
    node = fct_rev(node)
  ) %>%
  select(-malaria_percent1) %>%
  left_join(
    sankey_seropositive_answ1,
    by = c(
      "next_x" = "malaria_behavior1",
      "next_node" = "malaria_answ1"
    )
  ) %>%
  mutate(next_node = malaria_percent1) %>%
  select(-malaria_percent1) %>%
  ggplot(aes(
    x = x,
    next_x = next_x,
    node = node,
    next_node = next_node,
    fill = factor(node),
    label = node
  )) +
  geom_sankey(
    flow.alpha = .8,
    node.color = "gray30"
  ) +
  geom_sankey_label(size = 4, color = "white", fill = "gray30") +
  scale_x_discrete(
    labels = str_wrap(
      c(
        "How many times do you think you have had malaria in your life?",
        "If you had malaria, would you go to the health facility?",
        "Recent exposure to any type of plasmodium"
      ),
      30
    )
  ) +
  theme_sankey(base_size = 18) +
  innovar::scale_fill_innova("npr") +
  labs(
    x = NULL,
    title = "Behaviour associated with recent malaria exposure"
  ) +
  theme(
    legend.position = "none",
    plot.title = element_text(hjust = .5),
    plot.margin = margin(5, 5, 5, 5)
  )

```

```{r eval=FALSE}
ggsave("./02_output/plots/plot34_sankey_seropositive.png",
       sankey_seropositive,
       height = 7,
       width = 14,
       dpi = 300)
```

## Relation gender and ocupation

### By 4 Malaria

```{r}
sero_4malaria_district_gender_economic <- seropositivy_summarise(
  ffi_total,
  "4malaria",
  economic_activities,
  gender
) %>%
  ggplot(
    aes(
      x = economic_activities,
      y = result_malaria,
      color = malaria,
      group = malaria
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(gender),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    limits = c(0, 0.40)
  ) +
  labs(
    x = "Economic Activities",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw() +
  theme(
    axis.text.x = element_text(
      angle = 45,
      vjust = 1,
      hjust = 1
    )
  )

sero_4malaria_district_gender_economic
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/plot35_sero_4malaria_district_gender_economic.png",
  sero_4malaria_district_gender_economic,
  dpi = 300,
  bg = "white",
  width = 11,
  height = 7.5,
  scale = 0.9
)
```

### By Type of Malaria

```{r}
sero_typeofmalaria_district_gender <- seropositivy_summarise(
  ffi_total,
  "typeofmalaria",
  age_cat,
  gender
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_exposure,
      color = exposure,
      group = exposure
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(gender),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    limits = c(0, 0.45)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

sero_typeofmalaria_district_gender
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/plot11_sero_typeofmalaria_district_gender.png",
  sero_typeofmalaria_district_gender,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)
```

### By Time of Malaria

```{r}
sero_timeofmalaria_district_gender <- seropositivy_summarise(
  ffi_total,
  "timeofmalaria",
  age_cat,
  gender
) %>%
  ggplot(
    aes(
      x = age_cat,
      y = result_exposure,
      color = exposure,
      group = exposure
    )
  ) +
  geom_point() +
  geom_line() +
  facet_grid(
    vars(gender),
    vars(ffi_is_district)    
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    limits = c(0, 0.45)
  ) +
  labs(
    x = "Age",
    y = "Seropositivity"
  ) +
  guides(
    color = guide_legend("Malaria")
  ) +
  innovar::scale_color_innova("npr") +
  theme_bw()

sero_timeofmalaria_district_gender
```

```{r eval=FALSE}
ggsave(
  "./02_output/plots/plot12_sero_timeofmalaria_district_gender.png",
  sero_timeofmalaria_district_gender,
  dpi = 300,
  bg = "white",
  width = 12,
  height = 8.5
)
```


# Maps

```{r}
library(sf)
library(leaflet)
```

```{r}
ffi_total_gps <- ffi_total %>%
  select(
    ffi_is_cod_com:ffi_is_cod_ind,
    pf_recent:pv_historic,
    pv_exposure:historical_exposure
  ) %>%
  left_join(
    ffi_household %>%
      select(
        ffi_h_code_community:ffi_h_code_household,
        ffi_h_community
      ),
    by = c(
      "ffi_is_cod_com" = "ffi_h_code_community",
      "ffi_is_cod_household" = "ffi_h_code_household"
    )
  ) 

ffi_household_gps <- ffi_total_gps %>%
  select(
    ffi_is_cod_com:ffi_is_cod_household,
    ffi_h_community,
    recent_exposure
  ) %>%
  mutate(
    recent_exposure = case_when(
      recent_exposure == "Positive" ~ 1,
      TRUE ~ 0
    )
  ) %>%
  group_by(
    across(c(ffi_is_cod_com:ffi_h_community))
  ) %>%
  summarise(
    recent_exposure = mean(recent_exposure)
  ) %>%
  ungroup()
  # mutate(
  #   color_marker = case_when(
  #     recent_exposure == 0 ~ "red",
  #     TRUE ~ "black"
  #   )
  # )

ffi_household_gps <- ffi_household_gps %>%
  left_join(
    ffi_household %>%
      select(
        ffi_h_code_community:ffi_h_code_household,
        ffi_h_district,
        ffi_gps_long,
        ffi_gps_lat
      ),
    by = c(
      "ffi_is_cod_com" = "ffi_h_code_community",
      "ffi_is_cod_household" = "ffi_h_code_household"
    )
  ) %>%
  mutate(
    ffi_h_household_id = paste0(ffi_is_cod_com, ffi_is_cod_household),
    ffi_is_cod_com_label = paste0(
      "Cod: ",
      ffi_is_cod_household,
      " - ",
      str_to_title(ffi_h_community),
      " (",
      str_to_title(ffi_h_district),
      ")"
    )
  ) %>%
  st_as_sf(
    coords = c("ffi_gps_long", "ffi_gps_lat"),
    crs = 4326
  ) 
  

  
# colors_pal <- rev(innovar:::innova_palettes[["ecomst"]])
# pal <- colorNumeric(
#   colorRamp(colors_pal),
#   ffi_household_gps$recent_exposure
# )

pal <- colorNumeric(
  hcl.colors(17, palette = "zissou"),
  ffi_household_gps$recent_exposure
)

# icons_household <- awesomeIcons(
#   icon = "ios-home",
#   iconColor = "black",
#   library = "ion",
#   markerColor = innovar::innova_pal("ecomst", reverse = TRUE)(17)
# )

communities_sf <- communities_sf %>%
  mutate(
    ffi_h_community = str_to_title(ffi_h_community)
  ) %>%
  left_join(
    ffi_household %>%
      select(ffi_h_district, ffi_h_code_community) %>%
      distinct()
  )  %>%
  mutate(
    ffi_h_community_label = paste0(
      ffi_h_community,
      " (",
      str_to_title(ffi_h_district),
      ")"
    )
  )



household_communities_leaft <- ffi_household_gps %>%
  leaflet() %>%
  addProviderTiles(
    providers$OpenStreetMap,
    group = "OpenStreetMap"
  ) %>%
  addCircleMarkers(
    layerId = ~ffi_h_household_id,
    label = ~ffi_is_cod_com_label,
    color = ~ pal(recent_exposure),
    group = "Households",
    radius = 7,
    weight = 5,
    opacity = 1,
    fillOpacity = 0.1,
    labelOptions = labelOptions(
      style = list(
        "font-weight" = "bold",
        padding = "3px 8px"
      ),
      textsize = "12px",
      direction = "auto"
    )
  ) %>%
  # addLegend(
  #   title = "Recent Exposure",
  #   pal = pal, 
  #   values = ~ recent_exposure, 
  #   group = "circles", 
  #   position = "bottomleft",
  #   transform = ~ scales::percent_format(),
  #   opacity = 1
  # ) %>%<font-awesome-icon icon="fa-solid fa-location-dot" />
  leaflegend::addLegendNumeric(
    title = "Recent Exposure",
    pal = pal,
    values = ~ recent_exposure,
    position = "bottomright",
    orientation = "horizontal",
    height = 20,
    width = 100,
    decreasing = FALSE,
    numberFormat = scales::percent_format(),
    group = "circles"
  )  %>%
  addAwesomeMarkers(
    data = communities_sf,
    layerId = ~ffi_h_code_community,
    label = ~ffi_h_community_label,
    group = "Communities"
  ) %>%
  addProviderTiles(
    providers$OpenStreetMap,
    group = "OpenStreetMap"
  ) %>%
  addTiles(
    urlTemplate = "http://mt0.google.com/vt/lyrs=m&hl=en&x={x}&y={y}&z={z}&s=Ga",
    attribution = "Google Maps",
    options = leaflet::tileOptions(
      maxNativeZoom = 19,
      maxZoom = 20
    ),
    group = "Google Maps"
  ) %>%
  addTiles(
    urlTemplate = "https://mt1.google.com/vt/lyrs=s&x={x}&y={y}&z={z}",
    attribution = "Satellite View",
    options = leaflet::tileOptions(
      maxNativeZoom = 19,
      maxZoom = 20
    ),
    group = "Satellite View"
  ) %>%
  # Layers control*
  addLayersControl(
    overlayGroups = c("Households", "Communities"),
    baseGroups = c("OpenStreetMap", "Google Maps", "Satellite View"),
    options = layersControlOptions(collapsed = FALSE)
  ) %>%
  # addLegend(
  #   "topright",
  #   pal = innovar::innova_pal("ecomst", reverse = TRUE)(980)
  # )  %>%
  # addLegend(
  #   position = "bottomright",
  #   colors = c(
  #     "white; width:15px; height:15px;
  #                border:5px solid red; border-radius:50%;",
  #     "white; width:7.5px; height:7.5px; margin-top: 5px;
  #                border:5px solid black; border-radius:50%; margin-left:2px;"
  #   ),
  #   labels = c(
  #     "<div style='display: inline-block; height: 10px;
  #                margin-top: 8px;line-height: 10px;font-weight: bold;
  #                color: black; '>At least 1 person with recent malaria</div>",
  #     "<div style='display: inline-block;height: 10px;
  #                margin-top: 8px;line-height: 10px;font-weight: bold;
  #                color: black; margin-top: 10px;
  #                margin-left:5px'>No one with recent malaria at household</div>"
  #   ),
  #   opacity = 1
  # ) %>%
  leaflet.extras::addResetMapButton()
```


```{r eval=FALSE}
htmlwidgets::saveWidget(
  household_communities_leaft,
  file = "./02_output/plots/household_communities_leaft.html"
)

webshot::webshot(
  "./02_output/plots/household_communities_leaft.html",
  "./02_output/plots/household_communities_leaft.png",
  cliprect = "viewport",
  zoom = 4
)
```